How the high street gains from advanced data analytics

This is a guest blog post by Phillip Sewell, CEO at Predyktable.

The high street is finally back in business, with customers flocking back to stores, restaurants and pubs. To fuel and sustain this resurgence, Retail and Hospitality professionals are seeking smarter ways of adapting to the ever-changing needs of consumers, via multiple digital channels.

This means, critical calls are being made on how to personalise and differentiate consumer experiences and services, how to provide greater convenience – and the optimal resource levels to deliver this – all in a climate of extreme cost pressures.

To support this, there are huge amounts of rich data for retail and hospitality professionals to utilise. It’s all about converting data into insights that inform forward strategies and decisions to drive customers towards the high street.

Unfortunately, it’s easier said than done. Harnessing information and augmenting it with wider external economic signals and real-time localised insights, has always been the missing piece of the puzzle.

The data challenge

Most C-suite retail and hospitality professionals lack the data quality and predictive analytics capabilities needed to effectively support business-critical, forward decision making. This is also the conclusion of our research that canvased the opinions of over 100 senior data executives, with over 85% working in Retail and Hospitality.

Most are overwhelmed by vast data volumes offering little or no recommendations of what it means to them. Many executives also use solutions that aren’t tailored for their specific forward-thinking needs because they are heavily reliant on historic data and insights. For example, due to the Covid black hole, many are making forecasts based on five-year-old sales data.

There’s also not enough focus on identifying and understanding wider external data sources. This means executives miss out on valuable holistic insights of the market landscape, competitors, emerging trends and more. So, the quality and depth of insights aren’t there to support accurate predictions.

It’s no surprise that over 65% of executives say their data analytics tools aren’t providing best value across the business.

Advancing data analytics

To solve these issues, there’s an increasingly sophisticated prescriptive analytics capability, which when offered as a service, is the most advanced stage of data analytics’ evolution. Here’s where it sits:

  1. Descriptive analytics – what happened?
  2. Diagnostic analytics – why did it happen?
  3. Predictive analytics – what might happen in the future?
  4. Prescriptive analytics – what should we do next based on predictions?
  5. Prescriptive analytics as a service – what do we do and how can this be actioned and implemented?

Prescriptive analytics as a service not only predicts future events with precision but also provides clear and actionable recommendations on how Retail and Hospitality executives can achieve optimal outcomes for the high street. The best services achieve this by using pre-built, highly sophisticated, machine learning prediction models, enriched with wider global, regional and localised insights.

We’re talking about global & local trends such as economic, demographic, travel, weather, industry and localised demand spikes. Valuable insights are generated that feed approximately 70% of the prediction outputs for organisations. Outputs also dynamically adapt and update in real-time with ever-changing market conditions.

Data scientists and sector experts then address the remaining 30% by augmenting external prediction model insights, with a retail or hospitality organisation’s own data, customised to meet specific business requirements.

A better understanding is gained on the historical impact of an organisation’s actions on their results – compared to the wider market. By examining these variables and their relationships, previously hidden, connected, patterns and trends are revealed to generate foresight that fuels increasingly accurate recommendations on future actions.

Boosting the high street in action

So how do prescriptive analytics models help increase conversion across retail and hospitality venues? They involve analysing historical sales data, footfall trends, shopping patterns, and purchase behaviour.

This information is enriched with global data including how stores, pubs, and restaurants are affected by seasonality, inflationary pressures, transport strikes, social sentiment and more.

Individual stores or venues 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?

More dynamic effects are also considered, such as weather, tourism, travel disruptions, proximity to transport and event hubs.

All this information is combined with advanced machine learning models to reveal what regional demand could look like.

Equipped with this foresight, means more profitable high-street boosting decisions can be made on inventory, strategic product placement, promotions, pricing and staffing – for each store or venue.

Final thoughts

In the current economic climate, where every big decision really matters, applying gut instinct and guesswork isn’t good enough for predicting demand that boosts footfall and conversions back to the high street.

Prescriptive analytics offers meaningful insight to accurately predict and recommend where future actions will generate the greatest value. Ultimately, those retail and hospitality executives using it to augment their decision-making, have a greater chance of increasing shopping, drinking and eating across their high street stores and venues over the longer term.

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