Editor’s note: This is a guest post by Avinash Nair, a digital marketer at E2M.
E-commerce has been among the earliest industries to have successfully adopted predictive testing and analytics methodologies. The reason is pretty obvious. In a dynamic and competitive e-commerce space, getting to know the customer better and offering him the right products/services at the right time is the key to business success.
In an effort to retain customers and attract newer ones, online retailers are being proactive in trying to deliver targeted advertising and promotions that are more likely to convert. Although this sounds obvious, predictive testing is not easy and according to a research report by Ventana, only 13% of the respondents (2600 businesses) considered predictive analysis as a critical aspect of their business intelligence strategy. Predictive analysis is a specialized tool and it is beyond the capabilities of many people.
Nonetheless, predictive analysis is the need of the hour in an extremely competitive e-commerce environment where, without the right tools to predict the future, a business can simply run out of steam and peter away into nothingness. Not only is predictive testing helpful in knowing the customer better and help in product recommendations, it can also help in revenue forecasting and finding the true value of a marketing channel.
Historical data has its own limitations in an ever-changing marketing environment. This might not be the best source to predict future happenings. This is where predictive testing can come to the rescue. Case studies are aplenty that show successful use of predictive testing and analysis. E-commerce enterprises use it to better predict customer behavior and use these insights to serve better offers and promotions. They also use it for micro-targeting, attribution modeling, churn analysis and optimization, improved inventory management and in many other areas.
Best practices to use predictive testing and analysis
- Set specific goals
Like any good campaign, know what you wish to achieve before you start. This can be a challenge with predictive analysis, because most businesses are so enamored by the mystique around predictive testing that they fail to realize that it is not a magic wand that will solve every conceivable problem the business is facing or will face in the future.
- Have some foundational data to begin with
Most businesses feel that since historical data is unable to accurately predict future outcomes in a highly competitive e-commerce landscape, predictive testing is their end-all-be-all. Therein lies their biggest mistake. Remember that predictive testing is a statistical model that is based on historical data or any foundational data. If there is no supporting data, predictive testing cannot “predict” anything.
- Set small, realistic goals
Predictive testing takes time and is investment–intensive. 60% – 80% of the time allotted to a project is used in data collection. This could mean up to a year. If the project is grandiose, the earlier part of the project that does not show any results may not encourage the management to maintain elevated levels of investment. It is always worthwhile to set small, realistic goals, succeed and then build on from there.
- Have a deployment strategy in place
Okay, so now you have a stunning predictive model in hand. What do you do with it? You deploy it to your business to see the results the model predicted. This is where most businesses fail to see the model for what it is. It is a model that “predicts” what the outcomes will be only “if it is deployed.” The model won’t start working on its own.
Deployment strategies can range from simple to extremely complex depending upon the complexity of the current business models in place. This can lead to further investments that could run into a few thousand dollars. Thus many-a-times, the deployment of the model can be more resource-and time-intensive than building the model itself.
- Obvious results can still provide insights
Many times, predictive testing will reveal that most of the customers will come back without much intervention. Many businesses may “throw away” the model as it is just stating the obvious and they do not, apparently, have any use of the model. But predictive models can be very valuable in identifying deviations.
There might be anomalies that the business needs to look out for. For example a predictive model tells you that 95% of your customers will return and yet you find that a substantial number of high-paying customers do not return at all. This is an anomaly and has to be addressed. This is where proactive intervention is necessary.
To intervene, you need to first know why the customers weren’t returning. This is where you need more data. Only then can the business try to find the best way to get them back.
Predictive testing in e-commerce, or any industry for that matter, isn’t easy. It requires a combination of a robust testing method and human intuition and insight. Many consultants say that it is really hard to get it right. Mistakes will occur but the key is to remain flexible in mindset and accommodate any changes that occur along the way. The best practices stated above will help you form a strong base to build your predictive testing model.
Author bio: Avinash Nair is a digital marketer at E2M, India’s premium digital marketing agency. He specializes in Content Marketing and SEO consulting services. You can find him on Twitter: @AviNair52