Machine learning is the science that makes the critical decision for ECommerce today. If you are a small and medium-size ECommerce company, then you might have been avoiding machine learning until now. A crucial question for most ECommerce business owners is what to expect in return if you begin using machine learning? Machine learning models have significantly evolved today. Evolution of ML helps us get a clearer ROI perspective from ML and AI solution investment. There are five specific use cases that you are already addressing in your ECommerce business which can be better addressed by ML. You are likely to drive incremental business from each of these use cases with the use of ML.
Buyer Propensity Scoring
Buyer propensity scoring helps ECommerce marketers to come up with a precise metric of the likelihood of buying for a prospect user online. Propensity scoring matters because it can help you decide on following,
- Whether to engage the prospect further?
- How to engage?
- How much (budget or time to spend) to engage?
- Where to engage the prospect?
Propensity scoring helps you reduce the number of cart drop-offs. Buyer propensity scoring is a complicated exercise and has a dependency on myriad factors. Machine learning helps ECommerce companies to continually evolve their propensity scoring model as their data size and data features increase. To learn the effectiveness of buyer propensity scoring, identify the % of total buyers who were tagged as high propensity buyers by the ML model on daily/ weekly/ monthly basis.
When a customer buys or looks at one product, they might also be interested in other products. People who bought XYZ also bought ABC, the ABC, in this case, is derived through the machine learning model application to past sales or user behavior data. Product recommendations have a bearing on multiple internal and external parameters of customer behavior. All these parameters play a role in the user’s preferences to a specific set of products. Typically rule-based statistical algorithms for product recommendation keep giving static suggestions. For example, when someone buys tea powder, offer them an option to buy sugar. Buyer behavior is much more complicated than a rule-based recommendation algorithm. Machine learning works best when you want the model to consider multiple parameters for populating the recommendations. Every time the user purchases based on the recommendation offered to her, you have an incremental sale. Compare the results from ML-based product recommendation sales to rule-based recommendation sales to understand the effectiveness.
In simpler terms pricing based on demand and supply of the specific merchandise is dynamic pricing. Dynamic pricing helps ECommerce acquire customers when the merchandise is in the adoption phase; it helps clock more profits when the product is a cash-cow. Whether giving away the lowest price for a likely dropping off customer or selling at a premium for high propensity buyer, dynamic pricing directly impacts profitability and customer retention. There are many benefits of dynamic pricing for ECommerce that you might not have explored. Demand and supply itself are measured in multiple parameters, and therefore modeling the dynamic pricing using ML becomes appropriate. You can measure the effectiveness of dynamic pricing in terms of the total amount of incremental business you could acquire in a particular period as compared to the previous period.
Demand forecasting varies from industry to industry and depends upon multiple parameters. Demand forecasting helps in inventory optimization, which reflects straight into the profitability of your business. Demand forecasting helps product ECommerce companies to do precise production planning. Better production planning helps in reducing the instances of customers dropping off due to stocked-out items. Demand forecasting helps set correct targets on the month on month basis assisting the ECommerce turn into a more predictable business. Demand forecasting depends highly on internal as well as external factors. Demand forecasting needs to evolve as the market changes. For these two reasons, machine learning is an effective mechanism to build demand forecasting models as compared to rule-based static algorithms.
Customer acquisition is the priority for most ECommerce companies. For the long term, sustainable business retaining more customers becomes the priority. Retaining customers is possible when you can identify the customers who are likely to churn. Customer churn depends upon multiple factors such as customer satisfaction about the product they purchased, the service they received, the nature of the need, time of the year, and so on. Identifying customer churn using machine learning is a better approach to steadily evolve the understanding of which customer is likely to stick around more. ML-based customer churn approaches help retain more customers through the prioritized retention campaigns. To calculate the ROI on customer churn, you need to identify % of total buyers who were tagged as churning customers, and they made a purchase post retention campaign.
Machine learning can automatically account multiple parameters to make business decisions. And machine learning has a continuously evolving nature. These two critical features of machine learning models make them a favorite among ECommerce companies to animate above five use cases.
Datoin is an ML-driven AI platform for ECommerce companies. Datoin helps ECommerce companies experiment with data with the least investment of time, resources, skillset, and money. Try the value of your data with ML with Datoin.