The overwhelming power of analytics in retailing and B2C: Part II


Retailing and B2C market requirements for online insights are heavily relying on the closed-loop portfolio. The permanent and online interaction of analytics towards rolling planning or predictive models applies all the time. As a follow up on Part 1 on the overwhelming power of analytics in retailing, we talk in this article about the various ways analytics is applied in retailing and B2C. The situation discussed below are far from exhaustive, but at least it provides the insights on what I have experienced in various engagements with the retailing sector.

More than anywhere else, the retailing and B2C segments rely on real-time availability of data insights. Customer behavior, society influences, the distribution column; they all fluctuate intensely and affect commercial behavior so intensely, only real-time insights empower the retailer to monitor and adjust the closed loop portfolio. Needless to say retailing and B2C require in-memory platforms that both bring the calculation and data handling power, plus the scalability needed so badly.

At the base of getting insights are in-memory systems that track every single transaction done in the shops or online. An often seen solution for this is called SAP Customer Activity Repository (CAR). SAP Customer Activity Repository is a foundation that collects transactional data that was previously spread over mulitple independant applications in iverse formats. The repository provides a common foundation and a harmonized multichannel transaction data model for all consuming applications. Retailers can use SAP CAR to gradually transform their system landscapes from traditional database technology to the revolutionairy in-memory database technology. Assuming the real-time platform and CAR are in place, what is the typical scope for the retailing market segment's insights using analytical components from the closed loop? Let's have a look:

Basket analyses

Basket analyses are the core insights providing information what people buy at what moment and what location. We get insights in what is in their “basket”: especially the mix of products consumers buy is highly interesting. Using real-time predictive models, the retailer can predict whether a young female teenager buying red colored trousers might also be interested in purchasing accompanying earrings.

Sensor techniques help the shop employees to really focus their advice to the customer needs: sensors inform the employee that the customer is picking a blue shirt sized XXL from the rack. Online analytics and predictive models immediately tell the employee on a mobile device that the customer took the wrong size (based on his buying history) from the rack, but also typically buys 3 pieces (also based on his buying history). The employee is also informed that the customer’s profile indicates he might be interested in an accompanying jeans to the shirt he is looking at (based on predictive models). Loyalty card information indicates that if the customer today buys 4 pieces, an extra bonus is provided to his savings card. All this information helps the employee in the discussion and selling process to the customer.

Shop performance

With shop performance I mean the ability to use real-time closed loop analytics on an overseeing shop-level. Sentiment analysis based on for example an impacting television show last evening where a popular boy’s band showed their new hip colored sneakers, might trigger the retailing group to discount a second article when customers buy similar sneakers. The agility here is crucial: sentiments are notified from social media analyses and action needs to be taken immediately.

Local influences could mean specific sizes of a product are sold very well in one place, but less well in other places. This might trigger shop-management to reallocate stock to other shops. Similar applies to ranking capabilities: permanently monitoring top-bottom rankings per article, color-item or size is valuable, since the slightest social change (a big event in one specific city) might cause immediate changes in buying behavior locally.

Customer loyalty

Customer loyalty cards provide the retailer with a wealth of information if used well. The loyalty cards “identify” the person buying. We can see the consumer’s buying behavior, what (s)he buys and when. Tracking techniques (picking up the mobile devices signal when the customer enters the store), show us in real-time exactly where the customer spends the time in our shop, what is the average visiting time and what is the route a customer typically follows.

Retailers can go a step further combining loyalty card information with the customer’s buying history and add the customer’s social media information. This further completes one profile allowing the retailer to tailor made marketing initiatives on individual level. Me, customer X, receiving a special offer for a new External HD since combining data shows that I like audiophile equipment, buy music magazines (basket analyses) and spend quite some time at the electronics department when visiting the shop: I might need a storage device to store my music.

Customer loyalty cards might also bring great value to the customer retention program. Being enormously well informed having information available at any time, customers nowadays really quickly change providers of goods.

Weather influences

Weather conditions greatly impact buying behavior of customers. Windy weather in general proves to have highly negative impact on retail sales revenue. Making general statements is difficult since a specific weather condition can have a positive effect on one type of retail, and a negative on another. Think about cold weather that improves sales of books but negatively affects sales of handbags for example. A nice article focusing on the impact of temperature on this can be found here. Location – inbound or outbound – or the availability of underground parking with the retail shop is as much important in rainy conditions. For us important is to realize that weather conditions have so much impact, we cannot neglect them for our operational insights on shop performance, and thus make them part of the closed loop portfolio.

Alternative business models

Retail and B2C markets are probably one of the market segments highly interesting to follow. Why? …….. well they will be under great change. Digital Transformation and the availability of information to both the retailer as the consumer, change everything. Consumers not only wanting to know “everything” about their product, they also shift to buying (or should I say “renting”) a product-experience rather than the product itself. For the latter, think about the accompanying services to a product that the retailer might want to offer.

Have a look at this article from Forbes which tells enough. For me “food for thought” to write a part-III on the overwhelming power of analytics within retailing and B2C. Stay tuned. If you are looking for implementation models on the Retail segment, you should not miss this link.

Below is a little video of stunning results that I made in a 2 days exercise using self-service business intelligence on top of an in-memory platform with shop- transactions. I was impressed on how valuable the insights are with regards to shop performance and basket analyses. Very quickly I was able to track how different sizes of a specific T-shirt sell better or worse over various shops triggering the retailer to re-allocate the stock.

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