Predyktable

Advanced Data Analytics Partnership to Enhance Decision-Making in Retail and Hospitality

Posted on September 26, 2023July 29, 2024 by Phillip Sewell

ClicData and Predyktable have joined forces to revolutionise decision-making in the Retail and Hospitality sectors. They are both at the forefront of helping businesses to make correct business-critical decisions in the face of constantly changing, complex environmental, economic, and consumer behaviours. This industry-first partnership introduces a comprehensive data analytics solution that empowers customers to not only manage their entire data lifecycle, but to act swiftly on accurate prediction insights powered by Predyktable’s predictive models. This collaboration opens up profit-generating use cases for UK Retail and Hospitality brands including optimising spend, demand forecasting, marketing strategies, labour allocation, and more, all based on increasingly precise predictive insights.

Their approach transforms data analytics from hindsight into foresight, offering insights not just into what and why something happened, but also what might happen in the future and what to do about it. The UK’s expanding data reservoir presents an opportunity to harness data for predictive modelling. Real-time insights are essential for agile decision-making, and there is a growing need to bridge the skill gap in data and digital realms. This partnership offers a one-stop solution.

Predyktable leverages advanced AI and predictive analytics to provide a deep understanding of how consumer behaviour influences purchasing decisions. By aggregating a wide range of data, from global economic and environmental factors to regional indicators and industry-specific signals, their pre-built, Consumer-Behaviour Engine quickly builds accurate prediction outputs for organisations. Previously elusive, connected, patterns are revealed to generate foresight that fuels increasingly accurate recommendations on future actions. These results are automatically delivered back into ClicData’s platform.

“Our data platform excels at collecting, transforming, analysing, visualising and sharing any data: it’s powerful, smart and easy to use. We can now add further capabilities with Predyktable’s predictive analytics, so customers not only have a holistic and unified view of their organisation’s data, they now gain meaningful insight to accurately predict where their future actions will generate the greatest value.”- ClicData.

Phillip Sewell, Predyktable’s CEO and Co-Founder said: “This exciting partnership is about augmenting ClicData’s impressive data lifecycle management and analytics capabilities with predictive analytics. It’s the missing piece of the puzzle that drives more informed, contextual, future decision making in a climate of perpetual change. Our research reveals that 86% of industry executives view ‘predictive’ capabilities as their most sought-after features. This means an exciting range of use cases that unlock hidden insights, optimise processes, and drive profitable outcomes can now be achieved.”

In summary, ClicData and Predyktable’s partnership offers Retail and Hospitality brands the tools they need to make informed decisions, outperform competitors, and achieve exceptional results. With a commitment to client success and a dynamic Consumer-Behaviour Engine, they are poised to lead the way in the rapidly evolving world of advanced analytics.

About ClicData

Learn more at www.clicdata.com

Leveraging Large Language Models for Enhanced Contextual Understanding at Predyktable

Posted on September 18, 2023July 29, 2024 by Colin Crawford

1- Introduction:

In an ever-evolving world, Predyktable acknowledges the dynamic nature of our surroundings and its profound influence on consumer-business interactions. To navigate these changes effectively, we gather data from diverse sources, encompassing both structured data (e.g. weather and financial indices) and unstructured data (e.g. text and images) and input them into our data pipeline.

Structured data offers a straightforward modelling process, characterised by organisation and logic. For instance, it’s simple to assert that 20 degrees is warmer than 18 degrees. In contrast, unstructured data poses a challenge due to its semantic richness. Defining whether red is superior to green or quantifying the distinctions between Rock and Pop music in a numeric fashion can be intricate tasks.

2- The Role of Large Language Models (LLMs):

Large Language Models (LLMs) represent a category of artificial intelligence systems endowed with the ability to comprehend and generate human language. These models are meticulously trained on vast datasets comprising text and code, enabling them to grasp the subtleties of human language.

Although LLMs’ primary function is to generate information, in the form of chat or code generation, to do so it facilitates the conversion of contextual data into a numeric format that seamlessly integrates into predictive pipelines. For instance, using an LLM, we can encapsulate the disparities between a Taylor Swift concert and a Metallica concert. The LLM, with its linguistic prowess, has learnt that these events attract distinct audiences and can translate this understanding into numeric representations for more robust modelling.

3- Understanding Large Language Models’ Functionality:

LLMs operate by converting textual information into numerical values, subsequently subjecting these values to algorithmic computations—a process commonly referred to as tokenisation. Once tokenised, the LLM leverages its language proficiency to derive the meaning from the text.

For instance, when presented with the sentence “Taylor Swift is a pop singer,” the LLM dissects it, recognising Taylor Swift as a person, a singer, and an artist in the pop genre. It also comprehends the intricate relationships among these concepts. But in reality we don’t need to tell it who Taylor Swift is or how related she is to Kanye, it has already learned this information and can use this to tell us.

4- Advantages of Harnessing Large Language Models for Contextual Data Encoding:

Several advantages emerge from using LLMs to encode contextual data including:

Complex Relationship Capture: LLMs adeptly capture intricate relationships between diverse concepts.
Handling Unquantifiable Data: LLMs empower the representation of challenging-to-quantify data, like distinctions between different event types.

5- A real-world example:

To illustrate how Predyktable employs LLMs for contextual data encoding, consider this scenario:

Imagine Predyktable is partnering with a high-end women’s clothing retailer located in bustling urban areas. The retailer specialises in a wide range of women’s fashion, catering to diverse tastes and preferences. Their objective is to gain a comprehensive understanding of how various events occurring in their target market, influence their sales trends. To achieve this, Predyktable harnesses the power of LLMs proficient in language understanding. Here’s how the process unfolds:

Event Data Encoding: Predyktable starts by collecting data on upcoming events relevant to the retailer’s market. These events could encompass a wide spectrum, including fashion shows, cultural festivals, music concerts, and sporting events. For each event, the LLM is tasked with encoding critical information, such as:

• Event Type: This entails categorising the event, whether it’s a fashion show, music concert, sports game, or any other type.

• Event Date: Precise date information is recorded to establish the timing of the event.

• Event Location: The LLM captures details about where the event is taking place, whether it’s in the retailer’s city or another location.

Clothing Line Data Encoding: Simultaneously, the LLM encodes information about the retailer’s clothing lines. This encompasses a thorough analysis of their diverse product offerings, focusing on factors such as:

• Clothing Type: The LLM differentiates between various clothing categories, such as dresses, tops, pants, and accessories.

• Brand Information: It identifies the brands carried by the retailer, distinguishing between different labels and their respective popularity or prestige.

Building the Predictive Model: With the event and clothing line data successfully encoded by the LLM, Predyktable’s data scientists can proceed to build a predictive model. This model is designed to forecast how diverse events will impact the retailer’s sales. Here’s how this works:

• Event-Product Interaction Analysis: By leveraging the encoded data, the predictive model can analyse how specific types of events affect the sales of particular clothing items. For instance, it can identify whether fashion shows boost the sales of high-end designer dresses or if music concerts have a more significant impact on casual apparel.

• Time Sensitivity: The model considers the timing of events, ensuring that sales predictions consider both the event’s date and the lead-up time.

• Data Integration: It integrates the event data with other relevant factors, such as historical sales data, customer demographics, and marketing efforts, to generate comprehensive forecasts.

Ultimately, this predictive model equips the clothing retailer with invaluable insights. It enables them to make informed decisions about inventory management, marketing strategies, and event participation.

6- Conclusion:

Along with text generation and chat, Large Language Models serve as a potent instrument for numerically encoding contextual data, enriching predictive pipelines. Through the utilisation of LLMs, Predyktable elevates its capacity to construct enhanced models that better serve its clientele.

6.1- Further Considerations:

While LLMs continue to evolve, they have the potential to redefine our interactions with computers. Applications like chatbots, capable of comprehending and responding to natural language and precise machine translation systems bridging language gaps, are on the horizon.

Moreover, LLMs wield a substantial influence on the field of artificial intelligence, contributing to the development of innovative AI models like autonomous vehicles and medical diagnostic systems.

The ongoing evolution of LLMs holds promise for diverse and positive impacts across numerous domains, igniting anticipation for the transformative potential they bear on the world.

Why Now? The Perfect Time for Predictive Analytics in Marketing

Posted on August 22, 2023July 29, 2024 by Phillip Sewell

Introduction

The realm of marketing is in a perpetual state of flux. Emerging technologies, soaring customer expectations, and cutthroat competition have catalysed a landscape that demands nothing short of data-driven prowess. In this dynamic backdrop, the spotlight falls on predictive analytics – an instrumental facet of data science that is set to revolutionise marketing strategies. Predictive analytics leverages historical data to forecast future outcomes, enabling businesses to anticipate demand for goods and services, preempt shifts in customer behaviour, identify trends, and fine-tune marketing campaigns for optimal impact.

The question that beckons is: Why is now the perfect time for predictive analytics to flourish within the realm of marketing? This is the question that we will be exploring within this blog.

Predictive Analytics in Marketing with Predyktable

5 Critical Reasons to Adopt Predictive Analytics Now

While there exists a multitude of reasons to embrace predictive analytics, the five highlighted in this discussion stand out as the most relevant and beneficial in the current landscape. These considerations not only address pressing challenges but also offer actionable solutions that resonate with the contemporary needs of businesses.

The Abundance of Data: The Digital Era has bequeathed an unprecedented treasure trove of data, courtesy of the internet and the ubiquity of mobile devices. This data treasure can be harnessed to train predictive models that unveil accurate predictions about forthcoming behaviours and trends.
The Craving for Real-Time Insights: The rapid tempo of modern business demands nimble decisions to maintain a competitive edge. Predictive analytics can deliver real-time insights into customer behaviour, empowering businesses to make swift, well-informed choices.
The UK skill shortage: The UK’s advertising and marketing industry confronts a significant talent shortage, particularly in data and digital skills. This shortage poses a concern, especially as the UK is the world’s second-largest exporter of advertising services. Predictive analytics can help to address this talent shortage by making it possible for businesses to use data more effectively, analysing data and identify trends and patterns that would be difficult and extremely time consuming to identify manually.
The Soaring Costs of Digital Advertising: The escalating expenses associated with digital advertising necessitate targeted spending for optimal returns. Predictive analytics aids in precise targeting, enhancing the efficacy of marketing campaigns and yielding superior ROI.
Shifts in the Marketing Tech landscape: This complex intersection of data privacy, technological shifts, and ethical concerns poses a multifaceted challenge for businesses. Two areas in particular stand out as an immediate cause for change:

a. Mitigating Third-Party Cookie Impact: Third-party cookies, instrumental in tracking user behaviour for advertising purposes, face growing scrutiny due to privacy concerns. Browser phasing out of these cookies poses a significant challenge for businesses reliant on them. Predictive analytics offers a remedy, utilising historical data to predict user behaviour patterns, thereby circumventing the dependency on third-party cookies. This enables businesses to create accurate user profiles and preferences for more effective targeting and personalisation strategies.

b. Adapting to Evolving Social Media Landscape: Recent policy changes on social media platforms, the advancement of platform technologies, and customers who are more savvy with how businesses use their data, are impeding businesses’ data collection and utilisation efforts. Predictive analytics presents an adaptive approach by analysing historical data to identify customer behaviour patterns beyond social, using national mood and consumer opinion from an array of social, economic and industry sources. This insight forms the foundation for targeted marketing campaigns, which can circumvent the changing limitations of social media platforms.

3 Concrete Applications of Predictive Analytics in Marketing

Predictive analytics wields remarkable potential to elevate marketing strategies, and the moment to capitalise on this transformative power is upon us. While the applications of predictive analytics span a diverse range of business domains, it is within marketing where we see an increasing momentum in the application of advanced analytics. Here are three applications where forward-looking marketing departments are adopting predictive analytics.

Personalising the Customer Experience: By analysing vast quantities of historical data, as well as understanding how current consumer behaviour impacts demand, predictive analytics personalises experiences by suggesting products and services tailored to individual preferences.
Optimising Marketing spend: Optimises ad placement to ensure maximum impact and efficient use of ad spend. Identifies the platforms and channels where high-value audience segments are most active and allocates spend accordingly. By focusing on these channels, predictive analytics maximises reach and engagement while minimising unnecessary ad spend on less effective channels.
Predicting Customer Churn: Anticipating customers at risk of churning allows businesses to take proactive measures to retain them, safeguarding their client base.

These applications are merely the tip of the iceberg in terms of the transformative power of predictive analytics in marketing. To improve marketing outcomes, this tool is indispensable. Beyond these tangible advantages, predictive analytics brings a number of operational benefits to your business. These include:

Risk mitigation: Predictive analytics diminishes uncertainty, guiding businesses toward informed data-driven decisions.
Enhanced Efficiency: Automation and pattern identification, streamlines processes, making businesses more efficient.
Secure a Competitive Edge: Employing predictive analytics to make informed decisions empowers businesses with an edge over their rivals.

To Summarise

In the ever-evolving domain of marketing, staying ahead requires proactive innovation. Predictive analytics, with its ability to enrich decision-making, elevate marketing outcomes, and confer competitive advantage, is an indispensable tool. The time is ripe – embrace predictive analytics to navigate the complex currents of modern marketing with poise and precision.

The Three Pillars of Marketing Optimisation

Posted on July 25, 2023July 29, 2024 by Andrew Kohter

1. Introduction:

In today’s rapidly evolving business landscape, marketing optimisation has become an essential strategy for companies to stay competitive and achieve sustainable growth. The three pillars of marketing optimisation – customer high-value segmentation, personalised content, and ad spend allocation – play a pivotal role in maximising the effectiveness of marketing efforts. However, traditional marketing techniques have their limitations when it comes to these pillars. Fortunately, predictive analytics, when combined with external consumer behaviour data, has emerged as a game-changer, empowering businesses to overcome these challenges and supercharge their marketing optimisation strategies.

2. The Three Pillars of Marketing Optimisation:

2.1 Customer High-Value Segmentation:

Customer high-value segmentation involves dividing your customer base into distinct groups based on their value to your business. Traditionally, marketers rely on their own internal demographic data alone, leading to limited insights and an inability to handle complex customer data sets. Predictive analytics addresses these drawbacks by utilising advanced algorithms and machine learning techniques to analyse vast amounts of customer data from various sources. By integrating external consumer behaviour data, businesses gain a more comprehensive understanding of their customers’ preferences and behaviours beyond their own interactions.

Benefits of Predictive Analytics in Customer High-Value Segmentation:

Identifying hidden patterns: Predictive analytics uncovers previously unknown segments of high-value customers based on external behaviour indicators that are relevant to the business.
Real-time updates: Continuously analysing internal and external data provides real-time insights into customer behaviour, ensuring accurate and up-to-date high-value segments.
Precision targeting: Predictive analytics refines the segmentation process, enabling more precise targeting of customers with the highest potential value based on both historical interactions and current behaviour patterns.

2.2 Personalised Content:

Personalised content involves delivering tailored marketing messages, offers, and experiences to individual customers or specific segments. Traditional marketing often relies on mass communication and manual customisation based on gut feel, leading to lower engagement and inconsistent messaging. Predictive analytics, combined with external consumer behaviour data, revolutionises personalised content creation and delivery.

Benefits of Predictive Analytics in Personalised Content:

Advanced personalisation algorithms: Predictive analytics identifies patterns in external consumer behaviour. Allowing businesses to create more advanced personalisation algorithms and generate content recommendations based on consumers’ interactions with other brands and content types.
Cross-platform consistency: By considering external consumer behaviour across different platforms, businesses maintain consistency in personalised content delivery regardless of where customers engage with the brand.
Real-time content optimisation: Predictive analytics enables real-time optimisation of personalised content elements to meet consumers’ immediate needs and interests.

2.3 Ad Spend Allocation:

Ad spend allocation involves strategically distributing the advertising budget across different marketing channels and campaigns to maximise ROI. Traditional methods lack accurate measurement, limited real-time optimisation, and inefficient budget allocation. Predictive analytics, coupled with external consumer behaviour data, revolutionises ad spend allocation strategies.

Benefits of Predictive Analytics in Ad Spend Allocation:

Enhanced attribution modelling: Predictive analytics attributes conversions and key metrics to specific advertising channels, considering both internal and external consumer behaviour data, allowing businesses to allocate ad spend to the most effective channels.
External market trends: Analysing external consumer behaviour data helps businesses understand broader market trends and target emerging markets or new customer segments with high potential.
Real-time optimisation: Predictive analytics provides real-time performance insights, enabling marketers to adjust ad spend allocation on the fly based on changing market conditions and consumer behaviour.

3. Conclusion:

The three pillars of marketing optimisation – customer high-value segmentation, personalised content, and ad spend allocation – form the backbone of successful marketing strategies. Traditional marketing techniques have their limitations, but predictive analytics, when combined with external consumer behaviour data, offers a powerful solution to overcome these challenges. By leveraging advanced algorithms, machine learning, and real-time insights, businesses gain a deeper understanding of their customers, create personalised and relevant content, and allocate their ad spend more strategically, ultimately leading to improved marketing performance and business growth in today’s dynamic market.

Revolutionising Marketing Spend Allocation: The Power of Predictive Analytics

Posted on July 5, 2023July 29, 2024 by Andrew Kohter

Introduction

In today’s dynamic and data-driven business landscape, traditional approaches to marketing spend allocation are proving to be inadequate. Manual decision-making processes, once the norm, are plagued by limitations that hinder the effectiveness and efficiency of marketing
campaigns. However, there is a powerful solution on the horizon: predictive analytics. By harnessing the potential of predictive analytics, businesses can overcome the problems associated with traditional marketing spend allocation and unlock new opportunities for growth and success.

In this blog we will examine some of the key issues Marketing teams face today with using traditional methods, and the way that predictive analytics can remove the reliance on them.

Predictive Analytics vs Traditional Methods

Problem 1: Subjectivity and Biases in Decision-Making

One of the key issues with traditional approaches to marketing spend allocation is the heavy reliance on human judgment and intuition. Marketing professionals often make decisions based on personal experiences or assumptions, leading to subjective and biased choices. This can result in suboptimal resource allocation, wasted marketing budgets, and missed opportunities to reach the right audience. However, predictive analytics offers an objective and data-driven alternative.

Solution: Data-Driven Decision-Making
Predictive analytics empowers marketers to make decisions based on concrete data and insights rather than subjective opinions. By leveraging advanced algorithms and machine learning techniques, businesses can analyse vast amounts of customer and market data to
identify valuable patterns, trends, and insights. This data-driven approach ensures that marketing spend is allocated strategically, targeting the right audience with the right message at the right time.

Problem 2: Inability to Respond Quickly to Market Dynamics

Traditional decision-making processes often lack scalability and agility, making it challenging for businesses to respond quickly to changing market conditions such as recessions or economic growth, or customer preferences like sustainability, or cultural trends. By the time decisions are made, the window of opportunity for effective marketing campaigns may have passed, reducing their impact and relevance.

Solution: Real-Time Insights and Rapid Adaptation
Predictive analytics enables businesses to gather and analyse data in real-time, providing up-to-date insights into market dynamics and customer behaviour. By continuously monitoring and analysing data, businesses can swiftly adapt their marketing strategies, allocating spend
where it will yield the best results. This agility ensures that marketing efforts remain relevant, impactful, and aligned with ever-changing market trends.

Problem 3: Inefficient Resource Utilisation

Manual decision-making processes often struggle to effectively analyse and process vast amounts of data. Retailers generate an immense volume of customer and market data, including purchase history, demographic information, online behaviour, and social media interactions. Manually analysing this data becomes impractical and time-consuming, limiting the ability to identify valuable opportunities for targeted marketing and customer segmentation.

Solution: Algorithm Generated Customer Insights
Predictive analytics discovers valuable and useful patterns, trends, and insights from large amounts of data. It’s like searching for hidden treasures within a vast collection of information. By identifying patterns and correlations within customer behaviour, businesses can gain a deep
understanding of their target audience. This enables more precise segmentation, personalised campaigns, optimised ad spend and tailored marketing strategies, ultimately leading to higher conversion rates and customer satisfaction.

Problem 4: Lack of External Data Sources

Traditional approaches to marketing spend allocation often rely solely on internal data sources, limiting the breadth and depth of insights available. Without access to external data, businesses miss out on valuable information that can provide a more comprehensive understanding of the market landscape, competitors, and emerging trends. This lack of external data sources restricts the ability to make informed decisions and allocate marketing spend effectively.

Solution: Integration of External Data and Third-Party Sources
Predictive analytics enables businesses to integrate external data and leverage third-party sources to augment their decision-making process. By incorporating data from social media platforms, industry reports, customer reviews, and other relevant sources, businesses can gain a holistic view of the market. This enriched data ecosystem empowers marketers to make more informed decisions, identify untapped opportunities, and allocate marketing spend based on a broader and more accurate understanding of the market dynamics.

Problem 5: Inaccurate Predictions and Forecasting

Accurate forecasting and prediction are essential for effective marketing spend allocation. However, traditional approaches relying solely on manual processes often fall short in this regard. Predicting customer behaviour and market trends is challenging without leveraging historical data, relevant external data sources, and advanced statistical models.

Solution: Advanced Statistical Modelling and Predictive Algorithms
Predictive analytics empowers businesses to forecast and predict customer behaviour and market trends with a higher degree of accuracy. By analysing historical data, predictive models can identify patterns, detect emerging trends, and make reliable predictions. These insights enable businesses to allocate marketing spend proactively, optimise inventory management, and seize revenue opportunities.

Conclusion:

Traditional approaches to marketing spend allocation are riddled with limitations that can hinder business growth and success. However, by embracing predictive analytics, Organisations can revolutionise their decision-making processes. The power of data-driven insights, real-time adaptation, efficient resource utilisation, and accurate forecasting can unlock new opportunities, drive targeted marketing efforts, optimise return on investment, and enhance overall marketing effectiveness.

Choosing the right tools to unlock the power of Predictive Analytics in Retail

Posted on June 26, 2023July 29, 2024 by Andrew Kohter

Introduction:

In the rapidly evolving field of predictive analytics in retail, there is a tremendous opportunity to leverage the power of data-driven insights to stay ahead in a highly competitive landscape. However, selecting the right predictive analytics tools and technologies is crucial to maximise the benefits. In this blog post, we will explore the 5 key factors retailers should consider when choosing predictive analytics solutions tailored to their unique needs.

1. Integration with Existing Technology Infrastructure:

One of the first considerations for retailers is selecting solutions that seamlessly integrate with their existing technology stack. This includes point of sale systems, inventory management software, and customer relationship management platforms. By ensuring compatibility and smooth integration, retailers can consolidate and analyse diverse datasets, leading to more accurate and comprehensive demand forecasts.

Furthermore, integrating predictive models with existing technology infrastructure promotes operational efficiency. Rather than introducing disparate systems and duplicating efforts, integration streamlines data management and analysis processes.

2. Integration with external data sources

In addition to leveraging internal data, retailers can enhance their predictive analytics capabilities by integrating external data sources. These sources can provide valuable insights into market trends, customer behaviour, and competitive landscape. When selecting predictive analytics tools, retailers should prioritise solutions that offer compatibility and easy integration with various external data sources, such as social media, weather data, economic data, demographic data, and industry reports. By incorporating this external data, retailers can gain a holistic view of their market and customers, leading to more accurate and robust predictive models.

3. Scalability, Customisability, and Flexibility:

Scalability, customisability, and flexibility are critical factors for predictive analytics tools. Retailers should opt for solutions that can handle large volumes of data and adapt to changing business needs. Scalable tools ensure efficient processing and analysis of data, regardless of an organisation’s growth or fluctuations in demand.

Customisation allows retailers to fine-tune predictive models to align with their specific business requirements, product assortment, and customer segments. Retailers operate in diverse markets with varying customer behaviours and preferences. By customising predictive models, retailers can capture the nuances and intricacies of their customer base, resulting in more accurate forecasts and tailored insights. This flexibility empowers retailers to understand and respond to their customers’ changing demands, ultimately driving customer satisfaction and loyalty.

Furthermore, flexibility in predictive models enables retailers to adapt to the ever-evolving business landscape. Retail organisations experience fluctuations in demand, seasonal variations, market trends, and other external factors that impact their operations. Flexible models can accommodate these changes, allowing retailers to recalibrate their predictions and adjust their strategies accordingly. By being adaptable, predictive models can provide real-time insights and recommendations that reflect the current market conditions, empowering retailers to make agile and informed decisions.

4. Usability and Accessibility:

Usability and accessibility play a vital role in the successful adoption of predictive analytics tools within retail organizations. Retailers should prioritise user-friendly interfaces that enable business users and analysts to interact with data and models easily and intuitively. The availability of visualisation capabilities, interactive dashboards, and self-service analytics empowers stakeholders at different levels to derive insights and make informed decisions. Cloud-based solutions offer convenience, scalability, and real-time access to data, allowing retailers to leverage predictive analytics capabilities from anywhere, anytime.

An often overlooked but critical step in implementing predictive analytics solutions is change management. Retailers must consider how to effectively manage the organisational change that comes with adopting these tools. This involves training and upskilling employees, fostering a data-driven culture, and ensuring buy-in from key stakeholders. By addressing the human element of change, retailers can maximise the adoption and utilisation of predictive analytics tools, ensuring a smooth transition and long-term success.

5. Robustness and Accuracy:

The robustness and accuracy of predictive analytics tools are paramount. Retailers must evaluate the algorithms, machine learning capabilities, and statistical models employed by the solutions. These tools should demonstrate proficiency in handling various forecasting scenarios, including seasonality, promotional effects, and demand volatility. Continuous model monitoring, validation, and recalibration ensure the accuracy and reliability of forecasts over time, enabling retailers to make data-driven decisions with confidence.

It is essential to regularly reassess the model’s performance, evaluate its alignment with the current business environment, and update it with new data and insights. Incorporating external data sources and maintaining a feedback loop with subject matter experts can help ensure that the model remains relevant and provides meaningful predictions in a changing landscape.

Predyktable Data for predictive analytics in Retail

Conclusion:

By carefully considering these factors and selecting predictive analytics tools and technologies that align with their specific needs, retailers can unlock the full potential of data-driven insights. With the right tools in place, retailers can improve demand forecasting accuracy, optimise inventory management, and ultimately drive business growth. Embracing predictive analytics in retail is no longer a luxury; it is a necessity for staying competitive in today’s rapidly changing marketplace.

Are you ready to embrace the power of predictive analytics in retail and revolutionise your business?

Industry view: what’s really challenging retail & hospitality executives

Posted on May 5, 2023August 19, 2024 by Phillip Sewell

By Phillip Sewell, CEO at Predyktable

I recently conducted a series of interviews with senior executives working across both the retail and hospitality industries to gain a deeper understanding of the most pressing challenges and priorities they currently face.

As these industries continue to navigate an ever-changing landscape, it’s crucial to understand the perspectives of those at the forefront. From supply chain disruptions to shifting consumer preferences, the insights gleaned from these discussions shed light on the most critical issues facing these industries today.

Here are the why’s and how’s behind the tough decisions these senior executives face, with their fascinating insights distilled in a Q&A below.

1- Given the cost-of-living rise and increased costs throughout your supply chain, how will you remain profitable?

“Many CEOs are ex-CFOs, so unsurprisingly they’re dealing with the cost of living by finding ways to cut expenses and remove services – but without damaging sales or losing customers. In fact, across our outlets we’ve reluctantly increased prices by 10% to offset supply chain costs” CIO – Multi-channel Retailer

“As a direct-to-consumer business, we’ve also put-up prices due to a 500% increase in freight costs. We’re now hedging our bets with our supply chain: trying to lock in fixed prices for 5 years to offset the volatile market. We’re also exploring new territories to offset the challenges globally, and where to invest to reduce operational costs.” CEO – Retail

“Customers always want more for less, but prices are going up and promotions are being increased in what has traditionally been a high peak end of season and new season. This is an indicator of how pub and hotel operators are struggling.” MD – Hospitality

Increased prices in retail and hospitality

“As a multinational restaurant chain, we are changing fees to align more to market realities. We need to focus on new business, we’re extending reach beyond our current portfolio – while growing revenue from our existing customer base.” MD – Hospitality

“As a DIY retailer, we need a more agile, flexible supply chain. We’re focusing on what’s driving value, so we’re looking at things like optimising demand forecasting. We are raising prices and measuring the sensitivity of this, while finding ways to reduce supply chain costs. We are also either reducing advert spend or making it work better.” Marketing Director – Retail

“It’s all about price. We’re having to increase prices by 13-14% per annum across our restaurant brands. It’s difficult to get the second visit during the week, so our pricing is keener. It’s a perfect storm of costs and balancing acts.” Marketing Director – Hospitality

“We have raised prices, but not too much as we’re a price-sensitive confectionery brand. We’re taking a hit on margin and hope it comes back. In the short term, we’re managing costs to mitigate this. It’s survival of the fittest, you try to hoover up market share and hope you retain it in the longer term.” Chief Growth Officer – Hospitality

2- What other issues are you facing today and what are the long-term impacts?

“We must be price sensitive to consumer’s expectations. We’re asking things like what people are willing to accept? How do you quantify the impact service quality has on price points? Managing costs will be critical, and staffing impact in the long-term is a concern. We need to better predict what the labour market will be like in 5 years-time and what changes in our recruitment model can mitigate against this.” CIO – Multi-channel Retailer

‘Volumes are not where they were, and we’ve been hiking prices. There’s still a role for pubs for informal occasions versus restaurants, but it’s all about getting people through the door.” Marketing Director – Hospitality

man and woman having dinner at restaurant

“It’s all about where to find new business. Customers are no longer loyal, basically businesses are just “swapping” customers and not stimulating new growth. We need new revenue and new customers.” Digital Transformation Director – Retailer

“Online will not hold the dominance it once did as the cost of online is becoming less feasible and concerns on the environment increase. We may see a shift back to bricks and mortar to deliver a greater experience.” Global VP of E-Commerce – Retailer

“There is a danger of oversupply in the market for restaurants. After many closures during covid, there’s been some aggressive new openings with new operators mostly in city centres. I think there will be an implosion. Pubs have really got their act together and are well placed to challenge restaurants, they also suit people when they’re working from home.” Chief Marketing Officer – Hospitality

Recessionary impact and labour availability are big issues. Everyone in the industry is suffering, with chefs being the most difficult to recruit. We’re using some central kitchens to produce food consistently and reduce the impact at restaurant level.” Chief Growth Officer – Hospitality

3- What are your key priorities and investments over the next 3 years?

“Technology investment is key. We’re examining which technologies can return ROI – while solving the biggest problems we have. We do need to better understand which areas require investments to plug the leaks in costs.” CIO – Multi-channel Retailer

“Digital tools and online is one area of investment for us, coupled with systems to help labour scheduling. It’s all about making the central and pub teams become more efficient. Capex is being maintained, but it’s now focused on maintenance and improvement or conversion to new offers – rather than new builds.” Marketing Director – Hospitality

“We’re focusing on improving the supply chain. It’s the biggest cost centre and has the biggest negative impact on customer experience. Over 45% of customer care calls cover where is my product? So, having a fantastic supply chain would help address this.” Digital Transformation Director – Retailer

“We’ll be investing in systems including ERP, PIM and re-platforming, to reduce the friction of doing business and enable scale and agility. Improving staff wellbeing is also key, especially as the fight to retain staff becomes increasingly critical. We are improving performance marketing that better connects with customers. Acquisition will also prove key, as the competition becomes increasingly fierce.” Global VP of E-Commerce – Retailer

“We’ll be driving like for like sales, including investing in the fabric of the building or in-restaurant technology that hits our sweet spot. Potential acquisitions are a consideration, with a focus on small operators with decent brands and locations. We’re also trying to find the sweet spot of recruitment and we’ll invest when we’ve got it right.” Chief Marketing Officer – Hospitality

Staying relevant and interesting is core to our strategy. We need a competitive edge versus competitors, so we’ve got to work out what that is and then make it relevant.” Chief Growth Officer – Hospitality

Final thoughts

The COVID hangover means that everyone still has a short-term mentality. That is the sentiment from all those I spoke with. Profitability is now the short-term goal, rather than longer-term strategic planning that existed pre-COVID.

So, with key decisions on spend, labour optimisation, demand forecasting and more, how about the efficacy of current solutions that support decision-making?

All agree that business intelligence and data analytics have helped retail and hospitality executives understand and influence their customers’ buying habits – but only up to a point.

Despite billions of pounds spent globally on data platforms, data repositories and a whole stack of tools, most still lack the help they need to turn data into forward actions that maximise profits. Everyone agreed that more ‘prescriptive’ data insights are urgently required by brands: providing forward recommendations that support more profitable business-critical decisions.

Why smart retailers are checking out prescriptive analytics

Posted on February 22, 2023July 29, 2024 by Phillip Sewell

“Most retailers are overwhelmed with vast data volumes offering little or no recommendations on what it means to them.” says Predyktable co-founder and CEO Phillip Sewell

For many retailers trying to navigate a climate of perpetual change, making correct business-critical calls on complex environmental, economic and consumer future outcomes is an expensive gamble. This is because current business intelligence and data analytics approaches that support retailers’ decision-making, no longer cut it.

Traditionally, business intelligence and data analytics have helped retailers understand and influence their customers’ buying habits. But despite billions of pounds spent globally on data platforms, data repositories and a whole stack of tools, most retail professionals still lack the support they need to turn data into forward actions that maximise profits.

Most retailers are overwhelmed with vast data volumes offering little or no recommendations on what it means to them. Many also use solutions heavily reliant on historic data and insights that aren’t tailored for their specific forward-thinking needs. There’s not enough focus on identifying and understanding wider external data sources. This manual time-consuming research isn’t being done, so the data quality and depth aren’t there to support accurate predictions.

These approaches aren’t enough to help retailers form a clear future view and know what to do about it.

A forward-looking catalyst

To solve these issues, there’s an increasingly sophisticated capability that’s taking data analytics way beyond explanations and predictions. Welcome to ‘prescriptive analytics’, which is widely considered the fourth stage of data analytics’ evolution. Here’s where it sits:

Descriptive analytics – what happened?
Diagnostic analytics – why did it happen?
Predictive analytics – what might happen in the future?
Prescriptive analytics – what should we do next?

Prescriptive analytics aims to look into the future and then recommend the best course of action.

Marks & Spencer and John Lewis are among a growing number of retailers using prescriptive analytics to ‘look into the future’ and pre-empt trading conditions in the weeks, months and years ahead. For example, M&S uses this approach to guide its design, buying and pricing decisions across thousands of product lines in 50 categories, including apparel, lingerie, footwear, accessories, food, home and beauty.

Why prescriptive analytics delivers more as a managed service

When it comes to outsourcing this capability, many retailers miss out by working with conventional data providers offering prescriptive analytics as a bolt-on, one-off piece of work – with minimal support. I believe that achieving valuable results with prescriptive analytics isn’t possible with off the shelf or piecemeal solutions that treat retailers as commodities.

It’s better opting for a partner offering prescriptive analytics as a fully managed service, backed by retail sector experience. They must focus on understanding a retailer’s business and specific challenges – as these are crucial factors underpinning success. Retailers must also be supported every step of the way, so they keep solving new challenges facing their business.

The best prescriptive analytics services blend descriptive, diagnostic and predictive insights, with cutting-edge artificial intelligence, machine learning, automation, genuine data science and in-sector consultancy expertise. Everything should be custom built, with each step creating prescription models precisely choreographed to meet retailers’ individual needs.

This means enhancing internal data, with much wider external insights including global & local trends: weather, travel, localised demand spikes, and more. Using this high-quality data, data scientists build and optimise prescription models which identify previously elusive, connected, patterns to deliver the most accurate foresight fuelled prescriptions.

Expect data scientists to continually find new insights to keep models relevant, while learning from the data so they keep delivering value. By uniquely aggregating data from a wider range of external sector sources, models are further enriched to provide greater accuracy and depth to foresight, so the prescription models keep getting better and retailers keep making the most profitable business decisions.

Use case – customer sentiment

Here’s an example of how retailers can better gauge brand sentiment through the voice of the customer with prescriptive analytics.

Current analytics tools offer limited views on what’s being said about brand, as they mainly focus on social media analysis and sample surveys. They don’t show how retailers are perceived through all online and offline touchpoints. By not involving sentiment in predictions, means less accurate, decision-making.

A better approach is to create machine learning models connected to everywhere that customers are talking about the brand. This means covering online and offline channels, social media platforms, rating & review sites, search engines, contact centre logs, chat bots, blog posts, and more.

Natural language processing is then used to contextualise each interaction. This means establishing if it’s voiced as a positive, neutral, or negative opinion, if this opinion is shared by anyone else, and if so, what’s the commonality between them?

Sentiment and activity hotspots are gauged across customer segments, location, and channels. These insights are enhanced with domain models that track behaviours at a national and regional level. This means determining if brand sentiment is part of a wider opinion shift, or if it’s unique to customers – because of a retailer’s actions.

All this activity generates rich foresight that fuels recommendations on which new products to launch or territories to explore. By also dynamically forecasting demand, enables retailers to optimise the cost of entering new customer segments.

What gains can prescriptive analytics deliver for retail?

There’s huge value and so many positive outcomes to be gained with prescriptive analytics as a service, some of these include:

Know which areas to reduce cost: including marketing spend, labour optimisation and demand forecasting.
Understand exactly where to make more money within the most profitable customer segments.
Identify which customers are most likely to convert, then win them over with a hyper-personalised and engaging shopping experiences.
Retain high-value customers by recommending products and services that complement customers’ existing purchase history, interests and lifestyle.
Better optimise pricing at a regional level to maximise the profit opportunities.

Final thoughts

Whatever your size, Predyktable delivers prescriptive analytics as a fully managed service to generate actionable foresight faster, without complexity and compromise. To discuss how we can help your organisation make more profitable decisions, please drop us a line, we’d love a chat.

How to profit from prescriptive analytics in an uncertain world

Posted on February 2, 2023August 19, 2024 by Andrew Kohter

A recent family trip to a fairground gave me a different perspective on the challenges of retail and hospitality professionals dealing with customers: where all is never as it seems.

In the ‘fun house’ I was staring at the special mirrors where no matter the viewing angle, what was in front of me had little resemblance to what my mind expected to see. Staring back was something that looked vaguely recognisable, yet when I acted in a familiar way, it behaved in an unexpected manner.

Remove the flashing lights and over-sugared kids, and this experience struck me as a great analogy of what it’s like for retail and hospitality professionals trying to understand and meet customers’ ever-changing demands. The unexpected shifting of sentiment and expectations, coupled with the fact that the only thing that’s certain is uncertainty, means it’s very difficult to make good business-critical decisions with confidence.

Until now, retail and hospitality professionals’ decision making has been supported by business intelligence and retrospective data analytics capabilities. What’s lacking from these capabilities are clear, undistorted, short and long-term future views and knowing what to do about them.

Many of these solutions are constrained as they rely on historic data and insights describing something that has already happened. Why is this no longer good enough? It’s because of ever-changing customer expectations and a two-year gap in historical data due to Covid. It’s why you can no longer afford to look back to move forward.

Internal, rear-facing insight isn’t enough, you must examine how customers are behaving in the wider world, or why they’re not shopping, eating or staying with you. Brands must start utilising much broader external data sources to understand the impacts of social economic and environmental changes have on customer behaviour in general. Those brands that do, will better understand the value of their actions, compared to the results of inaction.

With ad spend increasing, the competitor landscape so high and margins so thin; not being certain of your decisions can be very expensive and you’ll miss big opportunities.

Moving forward with clarity and certainty

The good news is a relatively new, sophisticated capability, called prescriptive analytics promises to solve these challenges by looking into the future and then recommending the most profitable course of action.

We’re talking about prescriptive analytics as a managed service blending descriptive, diagnostic and predictive insights, with cutting-edge artificial intelligence, machine learning, automation, genuine data science and in-sector consultancy expertise. Everything is custom built, with each step creating prescription models precisely choreographed to meet an individual organisation’s needs.

This involves enhancing internal data with much wider external insights including global & local trends: weather, travel, localised demand spikes, and more. Using this high-quality data, data scientists build and optimise prescription models which identify previously elusive, connected, patterns to deliver the most accurate foresight fuelled prescriptions.

Data scientists also continually find new insights to keep models relevant, while learning from the data so they keep delivering value. By uniquely aggregating data from a wider range of external sector sources, models are further enriched to provide greater accuracy and depth to foresight.

Ultimately, this means the prescription models keep getting better – so retail and hospitality professionals keep making the most profitable business decisions.

Use case – demand forecasting

Prescriptive analytics can help better forecast demand. Every retail and hospitality professional understands the importance of having the right product, in the right place, at the right time. But how do they make profitable decisions through the lens of regional demand?

It means digging deeper than just price, as customers’ expectations are also driven by availability, experience, and ethical considerations. It’s important for retail and hospitality professionals to ask the right questions. What you do? Who are your customers? Do you have stores? Where are they located?

This baseline information is then enriched with global data including how stores, hotels or restaurants are affected by seasonality, bank holidays, days of the week and more. Individual stores hotels or restaurants are isolated and modelled independently: answering how customers in these areas are behaving. Is it an area of growth? Will it be impacted by reduced disposable income?

Next up, more dynamic effects are considered, such as weather, tourism, travel disruptions, proximity to transport and event hubs. All this information is combined with advanced models to reveal what regional demand could look like.

Imagine a scenario where you could utilise external data that tells you the volume of people expected to attend an event near your venue or store. How about understanding the demographics of those attending and the types of products and services they would be interested in. Then imagine layering further insight on what’s driving this increased footfall past your door: such as local traffic disruptions, or weather conditions at this specific time and day.

Equipped with this foresight, means more profitable localised decisions can be made on staffing, inventory, promotions, and pricing.

Expect these gains

There’s so much value that can be generated with prescriptive analytics as a service, how about achieving these outcomes for starters:

Accurately forecast future demand
Enhance customer experiences
Boost sales and profits
Increase satisfaction and loyalty

Expect a return on investment in months not years. McKinsey’s research asserts that prescriptive analytics is poised to continue to deliver strong return on investment and become an increasingly important tool for businesses.

Final thoughts

Whatever your size, whatever your uncertainty, Predyktable delivers fully managed prescriptive analytics as a service. We generate actionable foresight faster to address your specific needs, without complexity and compromise. We’d love to hear how we can help your retail or hospitality brand make more profitable decisions.

Let’s Talk About the Power of Sentiment

Posted on January 27, 2023August 19, 2024 by Andrew Kohter

Sentiment analysis has never really gone away, but it’s certainly seen a strong resurgence as social media has grown to become a core channel for so many brands and consumers the world over. Before social media, where exactly did we garner data for sentiment analysis?

Sentiment Analysis and the birth of the internet

Believe it or not, SA has been around since the 1950s, when it was primarily used on paper documents. Over the decades, it’s closely followed the channels and communities in which we express ourselves. By the birth of the internet, the use of SA adjusted to include the early channels of the social web, such as forum posts and online articles.

Today, it’s difficult to comprehend just how many sources and data points can be included in SA. But this works in our favour for several reasons.

The more data we can gather, the more accurate we can be in our reporting
The more channels and sources we can monitor, the more broad and diverse our data
The more choice we have, the more we can customise our requirements

Putting the customer front and centre

Sentiment analysis is one of the most valuable exercises in making your brand or organisation more customer centric. It’s a direct line to the collective voice of the consumer, whether they’re bought from you, plan to buy from you, and even if they aren’t planning to buy from you.

We must remember that positive, neutral and negative data are all good data. Brand strategy isn’t just built on why people want your product or service, but also why they might not want it too. And it’s important to use that data to shape your marketing and ultimately, your own voice.

Is all this data too much data?

It’s a very valid question. Can we have too much of a good thing? In our view, the current SA landscape is a wild, wild west. With new platforms springing up left, right and centre (notable new additional in the last several years include sites such as Glassdoor and Polywork) and entirely new ways of sharing content can completely disrupt a channel (we’re looking at you, TikTok), it’s tough to keep up, and it’s even tougher to sift through so much data.

That’s where Predyktable helps. Traditional SA methods can be time-consuming, confusing and sometimes inaccurate due to undetected anomalies. Our model accounts for all of this and takes the hard work and frustration out of SA. In fact, we go a step further, taking SA way beyond social media, a point at which many traditional SA businesses stop and send you their invoice.

A method to the madness

The concept of monitoring such a broad range of sources, including blogs, reviews, call centre logs and even search engine terms, might be a little daunting. All of that unstructured data from so many different kinds of people using so many different channels.

This is where our data visualisation tool comes in, breaking down broad and complex models into easy understandable data, which most importantly, is actionable.

This quite literally gives you a clearer picture of pain points, frustrations, experiences and more. And with the increasing power of not only our tools, but the social channels that your customers are using, we’re able to segment by demographics such as region, gender, age and even lifestyle.

Imagine the future

Our sentiment analysis is different, and we’re incredibly proud of what we’ve built and will continue to build upon and improve. Imagine being able to understand your brand’s reputation across such a broad range of sources. Imagine being able to spot potential areas of growth and investment, and to be able to act upon them now rather than later.

And perhaps most importantly in this day and age, imagine being able to spot negative sentiment and being able to deal with it there and then, long before it spirals out of control and causes damage to your brand. (To be clear, we’re not talking about something malicious like covering up bad reviews, we’re talking about an interactive and agile approach to your brand strategy, and making positive improvements to your product or service as a result of negative feedback.)

It’s also important to consider how SA can open you up to more modern ways of marketing. For instance, imagine being able to identify key social media influences to champion your brand. In the age of influencer marketing, this isn’t a channel to be ignored. Our platform is so powerful that it will even ensure the influencers that you’re seeing within your data have been fully verified for reach and engagement, ensuring you work with the right people.

A voice of reason

In a noisy, crowded world of challenging reviews, increasing customer expectations and online channels where opinions fly overhead like rockets, it’s never been more important to harness the power of sentiment analysis. What’s difficult is finding the right people and platform to cut through the noise and make sense of it all.

Thankfully, that’s us. If you’re looking for a technology service that has its ear to the ground and will go the distance with you, let’s talk soon.