Retail![]()
Customers are a mysterious bunch. Sometimes it seems like an endless battle to retain them, let alone acquire new ones. How to understand them? How to meet their expectations, comprehend behaviours and sentiment? How to connect?
The trends are clear – consumers demand an ever more personalized approach, better service and easier accessibility to products. The ball is in retailers’ court, as they need to reshape their business models in order to meet their evolving and constantly changing needs. To catch the customer in their shopping journey, it is not enough to have a good product, you need to get to know your customer on a profound level.
And not only them – gaining control over the full business cycle has become a necessity as well. Like knowing your full product flow from suppliers to stores and knowing exactly when and how to launch a new one. Predicting shopping trends, forecasting demand, segmenting customers, optimizing pricing and promotions, monitoring real-time analytics and results, successfully predicting future performance of new stores and knowing exactly how to organize their interiors. Then again, retailers need to ask themselves how to optimize their business processes and make internal operations leaner. These are the things that will differentiate between winners and losers and determine the future for each player of the game.
It will all come down to how you cope with the challenges you face. Do you use intuition, advice or some proper insight you can get from your data? The challenges we see retailers face in handling their data-driven strategy are:
- A lot of data in silos and not necessarily of good quality.
How to separate the wheat from the chaff? And especially – how to do it fast enough to gain immediate use out of it?
- Retail is shifting massively to the web,
where there’s an increasingly available amount of data available. This gives retailers a chance to aggregate, explore, and use even more behavior patterns and other imprints that consumers leave behind. But the problem already arises during the first step of aggregating data since many are not aware of all the places their data resides.
- Not enough of either awareness or use of external data.
There has never been such availability of big data from third party sources, public market place data and global entities. Those can be golden and can significantly influence and optimize decision-making in traditional as well as web retail and drastically impact the outcomes.
Here are a few possibilities (and we are only at the tip of the iceberg here!) of where you can use data in retail:
RFM (recency, frequency, monetary) analysis
Analysis of how recently, with what frequency and what value your customers spend. It helps with customer segmentation, how they differentiate between each other and who is most likely to be loyal in the future. It can also answer questions such as what’s the best time, place, medium and format to talk to them about your product or brand or what kind of messages will motivate their purchase.
Market basket analysis and recommendation engines
Allows you to identify relationships between the items that people buy. It collects data from the purchase history of the customers, as well as from viewings, clicks, search queries and in-cart items. Based on that data, the engine can give suggestions and drive sales. For example, Amazon’s exquisite recommendation engine is generating 35% of its sales.
Location of the physical stores and their layouts
You can’t squeeze everything into one format. Here you can really have a go with the public information available. How profitable is the location and what layout to use here? Machine learning models that take into account outside information (wealth of residents living in the area, foot traffic, etc.) can help you in leaving nothing to chance and the success of these kinds of placements is much more certain.
Pricing optimization
Pricing has become a powerful tool for retailers to grow. But identifying the right price tag is a daunting task – if priced too low, it could cost margin but then again, if it’s too high it can cost customer loyalty. Advanced analytics guide retailers in setting up the right pricing strategy and effective promotions. With machine learning, you can even differentiate pricing between different store clusters or product category clusters. From our experience, this has an immediate and significant impact on your revenue since it grows sales up to 2%.
To maintain its competitive advantage, this company decided to focus more intently on customer needs and demands.
Realizing that data analytics is essential for gaining insights into what customers want, the company knew it needed to access more data, store it in a single centralized location, and have the power to access and analyze that data in near real-time.
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