Contributed by Iver van de Zand, SAP
Retailing and business-to-consumer (B2C) market requirements for online insights are relying heavily 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 to Part One, today I’ll discuss the various ways analytics is applied in retailing and B2C. The situations below are far from exhaustive, but at least they provide insights into what I’ve experienced in various engagements with the retailing sector.
Retailing and B2C segments rely on real-time availability of data insights. Customer behavior, societal influences, the distribution column—they all fluctuate drastically and affect commercial behavior so intensely, that 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, which provide the calculation and data handling power, plus the scalability, that are 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 multiple independent applications in diverse formats.
Watch the SAP Customer Activity repository video HERE
The repository provides a common foundation and a harmonized, multichannel-transaction data model for all-consuming applications. Retailers can use SAP Customer Activity Repository to gradually transform their system landscapes from traditional database technology to the revolutionary, in-memory database technology.
Assuming the real-time platform and SAP Customer Activity Repository are available, what is the typical scope for this market segment’s insights using analytical components from the closed loop? Let’s have a look.
Basket analyses are the core insights that provide information about what people buy at what moment and at what location. We get insights into what is in their “basket.” The mix of products consumers buy is especially interesting. For example, a retailer can use real-time predictive models to predict whether a young female teenager buying red trousers might also be interested in purchasing accompanying earrings.
Sensor techniques can really help the shop employees to focus their advice specifically to the customer’s needs. Consider this shopping scenario:
- Sensors inform an employee that a 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.
- The buying history then indicates that the customer typically buys three pieces at once.
- The employee is also informed that the customer’s profile indicates he might be interested in buying jeans to go with the shirt he’s looking at (based on predictive models).
- Finally, loyalty card information indicates that if the customer today buys four pieces, an extra bonus will be provided to his savings card.
All this information helps the employee interact with and sell to the customer.
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 re-allocate stock to other shops. The same thing 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 cards provide the retailer with a wealth of information if used well. The loyalty cards “identify” the person buying. We can see the consumers’ buying behavior, what they buy, and when. Tracking techniques (picking up mobile devices’ signals when customers enter the store) show us in real time exactly where each customer spends their time in our shop, what is the route that customer typically follows, and what is their average visiting time.
Retailers can go a step further by combining loyalty card information with the customer’s buying history and social media information. This further completes one profile, allowing the retailer to tailor make marketing initiatives on an individual level.
For example, I—as customer X—might receive a special offer for a new external hard drive from a retailer, 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 that shop. The data insight is that I might need (or want) a storage device to store my music.
Customer loyalty cards might also bring great value to the customer retention program. Customers nowadays really quickly change their providers of goods because they are enormously well informed.
Weather conditions greatly impact the buying behavior of customers. In general, windy weather has proven to have a highly negative impact on retail sales revenue. Making other 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 how cold weather might improve sales of books but negatively affect sales of handbags, for example. (A nice article focusing on the impact of temperature on shopping can be found at the Summit Blog.)
Likewise, a retail shop’s location—inbound or outbound—and its availability of underground parking are very important in rainy conditions. For retailers, it’s important to realize that weather conditions have so much impact that they can’t be excluded from operational insights on shop performance. Thus, they must make them part of the closed loop portfolio.
Alternative Business Models
Retail and B2C markets are probably one of the most highly interesting market segments to follow. Why? Well, they’ll be under great change. The “Digital Transformation” age and the availability of information to both the retailer and the consumer are changing everything.
Consumers are not only wanting to know “everything” about their product, they are also shifting 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 describes three trends for retail in the future—instant gratification, borrowing, and customization.
For me, there is enough food for thought to write a Part Three of the series on the overwhelming power of analytics within retailing and B2C. Stay tuned.
Follow me on Twitter – @IvervandeZand.
– See more at: http://blogs.sap.com/analytics/2016/01/06/the-overwhelming-power-of-analytics-in-retailing-and-b2c-part-two/#sthash.YIdYhjL9.dpuf