Cross Sell / Up Sell Probability Calculation with Enhencer
Changing the direction of a sale or upgrading it with a bundle is the ultimate goal of every retailer. Thing is that probability of these are tender to calculate even with enough data.
Most of the time sellers try to change your decision on products by suggesting an upgrade or additional product. If we factor out the goodwill of the seller, we may say that additional products will be more profitable for them. Thus, when a seller persuades you to upgrade your choice, this is called up-selling. However, when a seller points out additional products, this is called cross-selling. They are more profitable for the seller side. Nonetheless, this is not a single sided advantage. Customer may gain some benefits, too. Increasing customer journey and decreasing trips to shops are the main advantages for the customer. On the other hand, seller may irritate the customer with irrelevant recommendations; thus, may lose the chance of gaining profit. Hence, forecasting behaviors and calculating probabilities of customers to respond a trigger is vital to apply cross-selling and up-selling strategies.
Why Cross-Selling is Vital
Importance of cross sell comes from the basic economic notion: more is always better. That is, every agent in the game of consumption is happy. Seller is happy because additional sales has been made, which means more profit. While also customer is happy because s/he relieved from the chance of another trip to the shop. In e-commerce also, customer may get a nice bundle of products to appeal his/her desires, which will enhance the customer journey. Enhanced customer journeys mean more consumption in any book. Therefore cross-selling is vital, as it means more profits directly and indirectly.
Why Up-Selling is Vital
Same economic notion (more is always better) works here also. Again, seller makes more profit by endorsing an upgrade to initial product. While customer (mostly) will think that an upgrade would make him/her better off as it is (unquestionably) better than the initial intention. Reaching the correct calculations and significant probabilities, up-selling would be the backbone of any retailing activity. Backed with correct marketing strategies and accurate triggers, collecting whole consumer surplus would be feasible with up-selling. The importance of up-selling comes from this notion. It is a pillar for market economy activities.
How to Calculate Their Probabilities?
As mentioned, an irrelevant recommendation can irritate the customer to buy additional product or even the initial product. Therefore, seller must know who to convey recommendations in what degree. That is, a young customer buying a computer may happily buy a gaming mouse additionally. However, the same offer may be declined by a senior customer. Same is also true for suggesting upgrades. Of course, the calculation of probability is multifaceted for this. Best way to calculate a predictive probability is to have an enormous data, rigid model, and testing. Thus, collecting response rates to triggers, volumes of the transactions, and persona features is to first start of probability calculation of cross-sells and up-sells. Think next time you visit an e-commerce website, there would be a couple of bundle suggestions in-line with some upgrades. Those suggestions would be based on customer movements before you and your past transactions.
Enhencer’s Way of Calculating These Probabilities
Enhencer uses the latest method possible to conduct a predictive analysis on cross-selling and up-selling probabilities: automated machine learning. Importing the data sets is the only thing you must do to reach trigger response probabilities. Having records and data about persona features, past trigger response rate, and past transactions mold into a predictive model constructed via machine learning in Enhencer. Thus, even in the initial step of transaction intention, right consumer can be triggered to upgrade to a different alternative or to buy an additional product at the right time, all thanks to actionable insights derived from Enhencer AutoML.