Why would I pay for grocery analytics when retailer data is free?

Maybe you don’t need to? Many small businesses, with a tiny product range and only one or two salespeople, may find grocery analytics unnecessary. There’s no point paying for something you don’t need, just because leading brands do it.

It’s important, however, to distinguish between data and analytics. Many retailers provide their suppliers with some trading data free of charge; some of the largest grocers supply a huge amount of data, enabling very detailed analysis.

But data, without appropriate tools and methods is of no inherent value.

Data is the fuel needed for analysis, insight and action. It can transform an organisation from guesswork to accurate planning, execution and correction – if used well. The question, therefore, should be:

How can I make the most of freely available retailer data?

Great question! Here in the UK, and in other markets around the world, grocery retailers provide their suppliers with a rich dataset that describes shopper demand for their own products. This data describes how products moved through the retailer organisation into the hands of shoppers, or resulted in waste. The best retailers provide this data in great detail – typically every item, in every location, every day.

This data offers every supplier huge potential to identify and act upon opportunities and issues, daily. But data is just potential. To understand the behaviours it records, and choose appropriate actions that improve performance, you must analyse the data carefully to arrive at accurate, insightful and informative information.

There are many ways you can use this information, here are just a few:

  • Understanding how sales of your products are affected by price, availability and distribution across different types of stores, in different regions, at different times of the year so you can plan effectively and execute with confidence
  • Identifying where you have too much or too little stock, risking waste or missed sales opportunities so you can inform retailer staff and present compelling reasons for change
  • Helping retail buyers, merchandisers and stock controllers to understand how your products sell so that they can plan effective promotions and place appropriate orders
  • Determining optimal promotional activities, across multiple retailer customers, so you invest more of your budget in promotions that achieve the results that you want to achieve
  • Building a deep understanding of product performance – including how shoppers respond to promotions, new product development and price changes – and sharing compelling insights with your customers so you become a trusted advisor and strengthen the value of your retailer relationships

To achieve these goals you need to balance effort across four key areas:

  1. Build a strong foundation through reliable data capture and management so you can sense shopper demand for your products across your retailer customers
  2. Harness your curiosity and create an analytical culture where understanding and insight are prized so you can respond to shopper demand by supplying the right amount of the right products to the right places at the right time
  3. Use your insights to plan, execute, measure and adjust experiments with pricing, placement and promotion of your products so you can shape shopper demand for your products in partnership with your retailer customers
  4. Develop long-term plans for new products and retailer relationships so you can create shopper demand for your future product range

If you are committed to these principles then it’s important to ensure that your people have the appropriate technology to use the data you collect in effective and efficient processes. The data foundation described in [1] above is critical to your ability to address the other areas. Freely available data provides an abundant source of fuel but you will need the right tools to help your people analyse the data, interpret the patterns therein, understand the actions available to them and measure the impact of their selected actions.

The most commonly used tool for grocery retail data analysis – for ALL analysis, in all organisations, for that matter – is Microsoft Excel. We cover the basics of analysing grocery/ supermarket data using Excel in these articles:

How do I analyse supermarket data in Excel?

When should I stop analysing supermarket data in Excel?

From these articles, you can see that Excel is an excellent tool for learning the basics, and handling relatively small data volumes, but as your skills, needs and data volumes increase you will probably want to take a more automated, systematic approach using database(s) and visual analytics tools.

Your primary decision, once you choose to move on from Excel alone, is the classic “buy vs. build” choice that drives all application software decisions.

Buy vs. build for demand intelligence

Build your own

You can choose to build your own database, deploy your own business intelligence tool(s) and build your own solution. This is practical for smaller, simpler reporting applications e.g. sales reporting, stock visibility, service-level monitoring etc. The essential steps are:

  • Automate data collection from sources (daily/ weekly schedule);
  • Load data into a database, subject to appropriate validation, transformation etc;
  • Build reports over individual retailer datasets; and, optionally
  • Align some metrics across retailers and report consolidated views.

This is an approach that many IT/ data teams favour; it gives them complete control over the sourcing and processing of inbound data… but this control comes with responsibility for data quality, system reliability and change management that’s often overlooked. Gathering data from third-party systems brings multiple challenges, and many IT functions come to realise that they have bitten off more than they can comfortably chew – leading them back to the “buy” option…

Further reading on build your own: How do I build a SKU tracker?

Buy into a demand intelligence service

External providers can build, manage and operate demand intelligence services with a level of focus, reliability and responsiveness that no one CPG can afford. Successful demand intelligence providers are aggregating data every day from many sources, for tens or hundreds of CPG/ FMCG customers, and so can provide a fully-managed service at lower cost, and with higher reliability, than any one CPG can achieve. 

The trade-off is flexibility; managed providers design a best-fit solution to span their entire customer base, and the wider market if they are looking to grow, so will focus attention on common requirements. Their approach to customisation may be limited, and tends to follow one of these patterns:

  1. High-customisation of the entire experience – for a smaller customer base, and at a higher premium per customer
  2. User-configurable reports, dashboards and data export functions – allowing end-users to customise their experience to some degree
  3. Extendable service – with a common data model exposed to customers, so that IT/ data teams can extend and build their own demand analysis tools on top

Some providers enable all three approaches, either directly or through a partner network.

If you want to cover the most ground, at the lowest risk and lowest Total Cost of Ownership (TCO) it’s likely that buying into a demand intelligence solution, rather than building your own from scratch, is the most effective route.

Want to learn more? These articles can help: