Human attempts to evaluate data manually were doomed to failure from the very beginning. This can also be observed in online retailing at the moment: Until now, online retailers had the goal of collecting as much different customer data as possible in order to offer their customers personalized recommendations and individualized web stores.
They have now achieved that – at least the data part. But the fact is: most online retailers are not able to use their data effectively.
The reason is manual rules and filters. If a merchandiser decides that all products in the action sports segment are only relevant for the customer group of 18-39 year olds, a 60 year old will not get these products suggested. Even if he is interested and has already looked at new ski boots. An algorithm would recognize this user behavior and now recommend the best ski resorts to the customer regardless of his age and suggest suitable other products.
In the future, the analysis and evaluation of the customer journey in real time will determine the success of the online retailer. But in order to decide immediately for each customer what makes them want to buy, retailers need algorithms that create personalized customer profiles from the individual customer journey. The result is a precisely tailored offer for each customer. No merchandiser can do this manually.
Nevertheless, online retailers still believe they can get to grips with Big Data by sheer muscle power. This may work to a limited extent – up to a certain number of customers – and costs an enormous amount of working time. Every customer has his or her own customer journey that needs to be evaluated. Because this is not completely possible manually, customers are now divided into groups – with the result shown in the example above: a considerable number of customers “fall behind” and are ignored, even though they show a willingness to buy.
Even if a retailer were willing to hire an army of merchandisers to manually evaluate all customer journeys, they would never be able to make the right decisions within seconds. A good algorithm, on the other hand, works in real time and evaluates behavioral data instantly – deciding for all customers what to see after their next click.
And not only that. Thanks to artificial intelligence, algorithms learn and get smarter over time and can make even better recommendations. So the sooner retailers start using algorithms, the sooner they not only improve the customer experience, but also gain a competitive advantage over manual competitors.