Purchase Prediction

See what you currently can’t see about customers

What is purchase prediction?

Purchase prediction is the process of forecasting customers’ next purchases.

Generally, it helps businesses in two ways. Internally, it enables them to manage their demand and supply efficiently and effectively. This results in saving inventory costs and wastage. On the customer front, it helps them in delivering an excellent customer experience.

Thus, Purchase prediction utilizes machine learning algorithms to make accurate predictions.

Why is purchase prediction important?

Purchase Prediction assists in predicting the items, a customer is likely to purchase in their next visit. As a matter of fact, this prediction is performed based on the customer’s past purchases. This results in increased demand and inventory planning for the business.

Also, one hidden benefit of this technique is a reduction in inventory costs.

Moreover, you can leverage these insights to satisfy your customer’s needs in a predictable manner. The end result is customer delight and loyalty. Thus, it helps them deliver an outstanding customer experience.

For instance, Amazon’s one-third of revenues comes from this one feature of their platform.

Purchase Prediction Case Study for a Regional Delicacy Business

Client

An e-commerce company specializing in selling and delivering regional delicacies

Situation

The business selling regional delicacies was losing money every month because of the customer purchase behaviour unpredictability.

Task

The urgent objective was to stop losing money. The requirement was to understand customer purchase behaviour and plan the future course.

Action

We analyzed the past purchases by customers. Additionally, we performed analysis to identify items that were being bought by customers within a cluster. Purchase frequency, recency, and trigger events were important considerations.

Result

The insights were 180-degrees from the management’s perception about their customer preferences. While 68 items were on offer, less than 10 items were being purchased regularly. This was a real eye-opener for the management team (it took them 15-mins to digest the truth and grasp the real situation). This study helped them cut losses, pivot from their model, and take appropriate rebranding actions.

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