Online grocery shopping, personalized bonus-card ….. We all face these initiatives day to day. They are all very strongly driven by overwhelming analytics power behind them. This article is to share my experiences on them with you and what I learned from it. These are just examples of retailing and B2C customer journeys that I am part of. The below examples are not exhaustive at all; they are also not future but happen and are in production today!
One of the things that make the retailing market segment so interesting, is that it is extremely sensitive to community influences. Just a small thing might happen in society, and it immediately affects buying behavior: people are connected everywhere and any moment. A simple anecdote on social media is shared so quickly that it can influence consumer choices instantly. One simple bad message about for example a make of yoghurt, can raise or lower the selling of this product the next day. If the retailer wants to act upon these influences, he needs state of art Insights and online operational analytics.
Retailing is analyzing you
Your bonus-card combined with your social media credentials tell the retailer a whole lot more about you than you might realize. Analytics, clustering and predictive modeling inform the retailer about your family composition, your eating or clothing preferences, how many children and pets you probably have or even what kind of holiday you like. By smartly combining your information with reference groups, the amount of trustworthy information a retailer can predict is huge.
Now imagine that the retailer recognizes you based on your cellphone signal when you enter the store. This information is linked online to your bonus-card and social media credentials: “the retailer knows exactly-who is in the store”. Now based on the same cellphone signal, the retailer can follow (!) you through the store using GEO coordinates. It means the retailer knows you are in front of the vegetables section, and also knows – based on the bonus-card info – you like carrots a lot. The electronic banner automatically flips and messages about a special offer on “carrots that taste very good with this new white wine that you might want to try”, dedicated to you. Imagine?? Well ehhh .., forget about “imagine”: this is done today and you are part of it.
The truck-driver is heavily stuck
Imagine the latest game controllers are very popular, so our retailer decided to order an additional stock with one of his vendors. Using buying behavior and predictive algorithms, the retailer knows he will sell the controllers anyway. Early in the morning, the stock manager receives a message that the vendor’s truck driver is stuck at the border and will be very late. Order intake quickly searches for alternative vendors and place an online order. That order influences consumer prices and business analytics immediately predict the effect this price-change will have on today’s revenue. It also automatically adjusts the retailers forecast and rolling planning, even from its subsidiaries if they are there. Using basket analyses the new type of game controller might be influential to the selling of USB cables to so the retailer decides to order additional USB sticks and the system automatically adjusts distributed forecasts and rolling planning. Imagine?? Not at all!
Products on offer
Apart from understanding the buying behavior of a customer (using bonus cards and others), retailers spend a huge amount of effort in understanding where the demand will be. Trend forecasting algorithms combine social media posts, web browsing behavior and ad-buying data to predict what will cause a trend or buzz. Social media discussions on the clothing-habits of a popular band, might cause specific trousers to become popular. These sentiment analyses get even more complex if you realize there is a heavy demographic component embedded here, together with economic indicators. Offerings on thrillers will increase significantly if both the weather gets colder and in the same time a significant crime has been discussed on the social media.
In-Memory computing and Interactive Insights make the difference
Retailers and B2C’s in today’s market extremely dynamically follow-up and influence customer buying behavior. They have to because the consumer is so well informed and has so many alternatives for buying. Retailers have to act instantly on changing behavior. To do so the amount and complexity of information that needs to be analyzed, is so big, only in-memory computing can handle. Bear in mind also that a retailer is never on its own but part of a brand, meaning individual shop performance is rolled-up onto corporate level. This corporate level manages on online shop-performance indicators, compares the various stores and delegates rolling budgets down to the shops on a daily basis. These budgets vary daily given the changing demand-analyses we talked about above.
In addition the dynamics require online interactive analytical capabilities. Information on buying and demand behavior varies daily and is analyzes permanently. Ever changing sources, unknown structures of new information or simulation models require the analyst to interact with the data all the time.
In a next article, we will deep dive into some of the other use case for business analytics in Retailing and B2C market spaces. One of them is basket-analysis: using predictive modeling combined with business analytics, it is possible to on-line utilize the buying behavior of the consumer. Techniques that are used today! Looking forward to share with you.