Real data.
real time.
real handy!

What is Reapp?

Great business decisions are built on great data. And great data is sourced by Reapp.

Put simply, Reapp is a versatile analysis software tool designed to help businesses score sales and better manage stock. Reapp creates quick and easy automated reports, made direct from fresh data, to help create actionable insights that not only revolutionise – but reward – users.

Created by a team who live, breathe and practically dream retail analytics solutions (it’s a whole thing), Reapp is a fusion of bespoke software, innovative technology, and industry smarts – all working together to produce award-winning results for our clients. 

The Reapp retail analytics platform provides sales and stock insights in a matter of seconds, thanks to a suite of unique software apps that create at-a-glance reports and dashboards with a click. You can even build your own reporting sections, customising by branch number, waste, product and more – allowing you to drill deep into the data that matters most to you.

This gives you the ultimate edge when it comes to igniting your business strategy, streamlining inventory management, and planning – or even reactively pivoting – in-store marketing campaigns.

However you use analytics in retail, make sure you Reapp the benefits. 

Sound good? We think so too. 

Book a demo with our friendly team today. 

What is retail analytics?  

In short, retail analytics (or retail data analytics) is a method of providing brands with valuable insights that can be used to optimise almost every level of their in-store operations. These insights include: 

• Consumer behaviour and demand 
• Supply chain movement 
• Inventory levels 
• Historical sales

And plenty more. 

Using a variety of sources, such as electronic points of sale (EPOS), shelf space, planogram compliance and promotional materials, this data can then be processed into interactive reports – showing whichever form of analytics you’d like to focus on. 

In terms of advanced analytics, there are a few types that are used within the industry to take a closer look at how customers interact with your products.

Descriptive Analytics

The most common kind of retail data analytics, these numbers tell us exactly what’s happening under your brand’s nose. This organises raw data from multiple places (such as EPOS, ERPs, OMS and inventory monitoring programs) to create insights of shopping activity both past and present. 

Diagnostic Analytics

If you’ve got a why, diagnostic analytics will help you drill deep and find it. 

Using the same source data, this type of analysis requires number-crunching algorithms and clever AI in order to figure out what is causing ripples in your chosen area of focus. 

As such, diagnostic analytics is also used to search, spot and address problems as and when they happen, thanks to clever pre-set rules and parameters within the programming.  

Predictive Analytics

Sometimes you feel like you need a crystal ball. Predictive analytics can help with that. 

As the second most advanced retail analytics solution (the first being Prescriptive), these predictions come from data found by diagnostic and descriptive analytics. 

After all, to understand what’s happening next, our technology has to get a strong understanding of what has already happened – and, most importantly, what caused it.

From there, it can then detect exceptions and causations, accurately collating predictions and trends that can give brands the upper hand against competitors. 

Prescriptive Analytics

If we’ve covered past, present and future, the only thing left is prescriptive. 

This type of analytics – also known as the most advanced kind out there – effectively advises brands on what to do next to get the most out of their field sales solutions and maximise their investments. 

This system aims to determine, then follow, the best possible future outcome by running simulations on a number of conditions. We can also use algorithmic AI, tailored for brands and products, to create effective results through mathematical considerations. In the same vein, we can also utilise cutting-edge machine learning programs and software to successfully identify clusters and actions that lead to an increase in customer conversion – so you get the best of a multitude of possible worlds. 

At Reapp, we combine them all to produce reports that leave no shelf untouched.

The analytics we produce allows us to combine award-winning human insights, electrified with technology that lets us put hundreds of thousands of data units right at your fingertips.

As well as real-time insights, we give brands the power to react to results, saving precious time, energy and money when it comes to powering your planning process. 

How do retailers use big data?

We don’t mean to toot our own horn, but when the results are this good… you need the whole brass band. 

Reapp has helped some of the biggest brand names achieve amazing results. 

From boosting ROI for Quorn to growing overall retail sales value for Unilever, we are able to intervene exactly where you need us. 

But with big brands comes even bigger data. 

We have the tools to streamline these numbers and tailor them to meet your marketing goals and ROI aims, giving you crystal-clear reports that make perfect sense.

Whatever your area of scope, Reapp Performance, Analytics and Rewards can help bring it into focus. Like a magnifying glass. Or your nan’s reading glasses. 

Find out more about how Reapp has been working its magic in the real world, here.