Use data mining to predict if your product will crash and burn

Marketing based on consumer behaviour data is one of the most fundamental strategies companies employ to market their products. Then again, could these behavioural patterns of consumers in the market be always relied on? Could they be wrong at times?

That can be the case if the consumer base shows an initial inclination towards buying products that eventually fail. These consumers have been called “harbingers of failure” by Eric Anderson, a marketing expert, in a research paper published in 2015.

In the case study, Eric pointed out that people showed interest in Diet Crystal Pepsi and Frito Lay Lemonade. You may not have heard of them, which is exactly the point. They kept on purchasing the products while they could, which created a sandcastle of a structure portraying a growing demand and supportive market, which washed away in the long run.

The findings of the study were also in line with retail’s early adoption of big data. One data set covered more than 10 million transactions made over two years using customer loyalty cards, and the other covered 111 stores in 14 states and more than six years in aggregate. The authors looked at both sets of data from a national pharmacy.

These findings show unequivocally that multiple scenarios may occur, regardless of the initial performance of the product. This concludes that the consumer base’s moves are what matter rather than the product.

Most companies will only purchase data that they deem to be immediately useful. If you’re a cosmetics company, you’ll likely buy beauty-related data from a Nielsen or IRI market research firm, but that will not specify who is buying the same product in 2021.

Since data sets are typically strictly divided, locating cross-category data can be difficult. He said that a marketing team would purchase data on exclusively red drinks while he was employed at Ocean Spray. Anderson stated that “you did not notice anything related to cranberry juice in terms of other beverages.” “It was the red beverage database, not the beverage database,” that was at fault.

On the other hand, the harbinger effect has a tenuous connection to numerous real-world data mining and market colliding instances. It’s essentially a twisted version of basket analysis, in which consumers who appreciate product X are also inclined to enjoy product Y or combine the two in their basket.

As a result, it may affect customer lifetime value, a metric that enables businesses to identify their most valued customers and spend resources appropriately. Whatever the reason, harbinger customers appear to be beneficial to retailers. They will continue to shop regardless of how many times you advise them not to.) Categorising clients as red failure flags is a novel approach to segmenting customers based on particular characteristics or patterns.

• Basket analysis
  For decades, beer and diapers have been a common topic of discourse. According to the data-driven merchant, the two unrelated products were frequently purchased by the same client at the same time of day. They were young fathers who would treat themselves with a six-pack, either as an encouragement or save time and money by doing both simultaneously. Although the incident is probably certainly fabricated, it demonstrates how purchasing patterns impact organisations’ marketing techniques. According to a 2019 research by the analytics firm Quantzig, one European food retailer used a rule-based association dashboard to make real-time product bundling recommendations based on its different data sources. According to the business, the packaged advice increased advertising returns by approximately 300 percent. Basing your product analysis around a basket analysis like this will enhance your understanding of market placement. Thorough knowledge of consumer behaviour tells you what type of market your product needs to be surrounded by. Placing a product surrounded or close to a market that clashes with its basket analysis can increase its chances of crashing irrespective of its usability and demand. 


• Product Recommendation
  While product recommendation is similar to e-commerce, the packaged goods business refers to it as product placement. According to Mike Egger, AI and machine learning can learn how people tend to accept recommendations when they buy a product. According to the Journal, a natural-language processing architecture that merges “billions of historical data points” has evolved. If you’re a subscriber to a streaming service such as Netflix or Spotify, you’ve already seen how data mining for intelligent recommendation-driven engagement may succeed. How you watch a show that you like and then choose one of the recommended ones and like it. Hence, AI and Machine Learning can do your job of trying to recommend your products with others that are most likely to be bought together. This defines a constant increase in sales. Not incorporating AI and Machine Learning into your product sales can quite hinder your product’s potential by a ton.


• Customer Value
  When it comes to a company’s investment in customer service, not all consumers are created equal. The point is that companies shouldn’t spend money “trying to convert ugly ducklings into magnificent swans” but instead focus their promotional efforts on the value of each consumer. According to BV.Pradeep “market data analytics and consumer behaviour research should be seen as a continuum”. It illustrates the idea that the quality of consumers should weigh more than their quantity.  Your market analysis should in no way clash with your consumer attraction plan. Therefore, catering to customers based on their value rather than their numbers will go a long way in letting you know if your product will be there in the market to stay or crash right away.


• Customer segmentation
  Customers can be divided into distinct subgroups to tailor marketing messages and promotions to each group’s unique needs, which is an obvious benefit. However, segmentation cannot be random, and data mining identifies relevant client groups. Cluster analysis, a data mining approach, is frequently used in marketing analytics. K-means clustering is a common technique used by data teams to identify which data points are close together or far away in a distribution. The relevant customer personas arise from this investigation. From “mid-income, low annual spend” to “extremely high income, large annual spend.” It creates a clear picture of choosing the pricing range and a potential market for your product. Not all products are meant for everyone, and neither are all consumers meant for all products. Therefore, data mining and analysis will tell you where your product will fail and where it won’t. It doesn’t get any simpler.


• Churn prediction
  Keeping a customer is cheaper than acquiring a new one in the eyes of marketing. An attempt is made to predict the likelihood that a customer will either quash or not replenish a service, which marketers then use to try and prevent the turnover. Conventional key aspects of churn prediction are data mining techniques like regression analysis and classification. Companies can now use Python libraries like Streamlit to build classification/churn models with intuitive interfaces. With the increasing use of machine learning-enhanced churn prediction, it’s possible to get churn scores much further in the future. You can figure out how you’re going to deal with churn with your product shifts and how you will make it easier for the customer to work with you. As a result, it changes the conversation because it gives you enough time to plan, decreasing the chances of customers leaving for other products, saving your product from crashing and burning in the market.