Prevention is better than cure.
An important point to consider for a successful business is forecasting problems before they arise and act accordingly on the solution to further prevent losses a company may incur. Businesses have to take risks and suffer mishappenings if their customers pull out of a plan or scheme or return the newly based product.
An example is the digital telephone industry. This industry often witnesses a certain number of users return their products within the bounds of the “free-return” window. The majority of these customers state their reason for giving up on the product is due to the glitch in the item itself, but, studies and survey show deviating results. It is mostly due to lack of knowledge in operating the product, lack of realisation of specific utilisation or denial.
Several methods are introduced to seize this issue like for instance a conversation with the buyer after the purchase of the product and convincing him/ her by solving their problems and assuring 24/7 assistance. Alternatively, specific tech-friendly tools like Geek Squad Protection or Apple Genius also assist the consumers with their help. These ways are seen to be a lot time-consuming.
Business professionals now realise the importance of averting this problem through business analytics using a predictive model. This could help in transforming a possible returner to a permanent customer. Not only will it assist in preventing future losses but also in establishing a much appealing customer experience. Hence, ensuring a more delicate use of their customer and user experiences, essential for long-term allegiance and satisfaction.
Using Analytics to prepare for the unforeseen
“If you do not know how to ask the right question, you discover nothing.” -W. Edward Deming
Customers (without any intervention) were ranked from high to a low probability of being a returner by a predictive model. It was noticed that the top 10% of these customers with a high likelihood of returning actually comprised around 40% of returners on the list. Now, the intervention of the 10% is likely to impact the 40% of the possible returners, if future results reflect the reports based on the past data. It can be seen how cost-effective the above process is as the companies only made an intervention price for the 10%.
In cases of consumer dissatisfaction, the initial solution from the consumer’s point of view would be to return the product or get it replaced. The latter, on the part of the company, doesn’t hurt any kittens but the other one would undoubtedly add to the books losses. There, has to be a way out and intervention is perhaps the best way through this.
However, whether the act is to be implemented or not is decided by the economics of the trade-off between the cost of intervention and benefit of the same. If it were to be done, there ought to be a body to decide between the applicability of intervention and its counterpart. Artificial Intelligence is an attractive choice, to make an estimate of the likelihood of returners and benefit to cost ratio of the conversion taking place. The goal ultimately must be to enhance the decidability of the predictive model to supplement the sales of the product with product retention by the customers.
The issue of optimisation
Which intervention would be the most cost-effective? This is a pressing question faced by organisations today. A significant problem arises with the fact that the investments made by the concerned organisations to test different interventions on exclusive groups or people made prove to be in vain. Not only is the process a real long time operation but, also it may not provide the investors with a fulfilling record. This would be unprofitable for the company. However, the leading and advancement in machine learning may change the outlook when it comes to choosing the most favourable intervention.
Issues like disagreeable actions of the users or customers which lay intervention can buckle a poor impression of the companies and organisations. The expression “intervention analytics” wouldn’t add to any individual’s surprise by becoming the new drift.
How Can Predictive Analysis can help Retailers forecast intent?
What is the customer’s intent? Why is the customer likely to visit a particular store? What will they usually purchase?
Don’t count the chickens before they hatch.
Here we can say we can count them before they hatch. Courtesy: Prediction.
Predictive Analytics uses historical trends and patterns of the buyer’s past purchases or regular visits. The offline data particularly location data solves the issues based on explicit grounds. For example, a visit to the store by a customer. In this digital world, retailers can smartly make use of data-backed predictions on the activities of the customer. The merchants can have a pre-conceived notion of the customer demographic. However, with the predictive analytics, their efforts can be efficient to meet the future demand for a dedicated amount of customers.
- Visit Data
This type of predictive model makes assumptions on the number of visits, days since the last visit, the duration of the visit, locations of the visited stores, etc. Data serves the model in managing accuracy and fetch better predictions of future visits. This brings an applicable, hardback and analytical edge for retail marketers to know and classify visitors. Also, helps in maintaining the regularity of individual visitors.
- Particular Predicting
Predictive Analytics depending on location data, online and offline data can change the way to a noticeable degree. Knowing of who our customers are and by grasping much data impression, the retailers can notably and successfully predict the shopper’s visit within a term.
- Maintaining Certainty
Marketers can use the predictive analysis to customise and personalise the extent to a group of customers or an individual customer. This can be one of the most effective uses of marketing expenditure. For example, a coupon or a free coffee.
For any business, it becomes quintessential to have marketing strategies and schemes for every segment of customers. Here segmentation is based on the frequency of visitors. For example, a restaurant would like a visitor to frequent in his/her shop every day. In a restaurant, one would perhaps want to see all the seats filled, how will he/she reach there?
They have predictive metrics to the rescue. As far as shopping and or dining in is considered, there always are certain traits shown by people. It is these traits that need to be used smartly by, to identify and segment those customers as said earlier. Now once these segments are made then targeted schemes, coupons, discounts or offers can be sent to them under Customer Loyalty Program etcetera. This increases the probability of the customer then visiting the outlet.