To increase revenue and profits, all businesses try to acquire more customers and sell more products to existing customers. They work really hard as an organization to ensure that new customers continue to come and that overall sales volume and profitability continues to rise with time. Product & Customers are two pillars of a retail business. Where the later have finite Customer Lifetime Value(CLV) and everyone stops buying eventually.
Out of the two fundamental entities of retail business customers and products we seem to be focusing much more than their fair share on customers only. The fact is we don't own customers, we don't control customers.
Our products are very much our own. We own them. We control them. We introduce new products. In a scenario where every time a new product is launched it becomes an instant hit. All products introduced having potential to sell out fast and many would go on to become best sellers. If it becomes a reality a store would not have to be so customer centric in all its actions and business strategy.
Aided by advances in Artificial intelligence (AI) in retail field. An intelligent store of today can come very close to this utopia of a store having only the best sellers.
Customers come and go, even products which are selling today will not be there in future. What remains constant is the store. Every product sold historically is a learning exercise for an intelligent store. Just like every day that passes by and every event that happens in our life teaches us something and can make us ready for a better tomorrow.
In depth understanding of all aspects of all products selling today and ever sold is the starting point of the important journey to an ever flourishing fast selling products store.
“Product Behavioral Analytics is an analytical methodology that focuses on and analyzes the different behavioral aspects of products to realize and work upon the enhanced business opportunities.”
All the Life that evolved on planet earth has connection to each other. All life forms share basic DNA structure. While products come and go. In a typical store which is selling products of a particular type like fashion and apparel store or cosmetics. The type of product sold does not undergo a total change in a short period of time. Specific products change but the DNAs of all products do not undergo a major shift when collectively taken together.
So what can be done to analyze products so well to yield the best from them? What measures can be adopted to know the secret of selling every product? What can be done to empty shelf and fill it with other sell-able products?
Products have personality too!
Just like customers, products also have personality too as their behavioral aspects. Behavioral aspects give particular personality individuality or a specific DNA to each product and by their analysis– better knowledge and insights about products can be gained. Attributes like color, size, shape, vendor, manufacturer and price range are all important contributors to understanding the products. Combined together with name description and natural language processing along with advanced vector techniques can be applied to form similarity and grouping of products to identify the DNA each product has.
A specific DNA appeals to corresponding segment of customers. From all the historical behavioral data of purchase, browsing as well as email and even cart combined knowledge of which customer is most likely to buy which product in specific and in general what type of products or what DNAs of product can be found out by an intelligent store.
So what it takes to ensure new product launch is successful?
New product launch is a regular task for any E commerce retailer. To promote a newly launched products retailers try to sell to existing customer base along with trying to acquire new customers primarily by social media advertisement.
For a successful store with a large existing customer base it becomes easier to sell to existing customer base. Selling to existing customer base is six times cheaper than trying to acquire new customers. Why is this so? Answer is simple social media advertisements used as primary channel of acquiring new customers is a costly affair compared to contacting existing customer base on email, SMS or other channels.
Let us discuss what makes a new product launch a success with existing customer base. Not every one of existing customers is interested in that specific new product launched. So a segmented approach is better to focus on a specific set of customers. This segmentation can be on the basis of same brand, same color or some other set of similar properties retailer feels is important decision making aspect of the newly launched product. This is surely a far better approach than trying to announce to the whole customer base. Spamming your loyal customer base with irrelevant products is a big NO. Segmented campaigning is a must in modern retail functioning. A platform which gives the ability to combine different behaviors like people who browsed certain category of products but did not make any purchase in last one month and emails were not sent to them is an example of capability of a good marketing automation tool's segmentation ability.
Well can there be a much more superior approach than that? Artificial intelligence and predictive analytics advances in marketing and user journey automation tool like Enalito provides ability to know who are the most likely customers interested in buying a specific product even a newly launched product. Based on the advanced product analytics approach the underlying recommendation system engine can come up with most accurate set of customers to target for a new product too. Combining the similarity of products (content based recommender system) with across behavior of purchase and browsing of all customers (collaborative filtering) the hybrid recommender system analyzes possibly billions and more data points and can zero down accurately on the set of customers those who are really interested in this specific newly launched product. Even customers who have never purchased or browsed a similar product before may be interested and the system identifies them as likely candidates to target also. This is impossible to achieve in the previous approach of customer segmentation discussed.
In other words, every product launched in such an intelligent store is capable of knowing who are the exact set of customers those who really want to but it. A store full of such intelligent products is surely a store worth envy.
For acquiring new customers for the store and for selling newly product launched to potential customers online retailers advertise on social media like Facebook, Instagram and Google. If good amount of advertisement dollars are spent good sales happen. But the profitability vs revenue from sales balancing remains a challenge. Return on Advertisement Spent (ROAS) becomes the most crucial factor of revenue growth and profits for a retailer. In the long run ROAS can be argued as the single most important KPI which determines growth and survivability of an online retail business.
Let us discuss how the power of this recommendation system based on the advanced product analytics provide many fold better ROAS to the online store in a consistent manner over the period of time. Again the difference and far more superior approach come from the same underlying aspect as that of trying to sell to existing customer base. A good segmentation of existing customer base on the basis of all the behavioral aspects which can zero down on all the important characteristics of the newly launched product is possible. But at best this manual work is very difficult to do it consistently for all new products launched. It also requires very skilled analytics capability in the marketing team. A luxury online a very few top 1 to 3% of all retailers can afford. Even with those predictive analytical insights which give much more accurate customer base as discussed is not possible.
So from existing customer base the recommender system of Enalito can accurately zero down on customers those who are most likely to buy a new product launched. How does this affect ROAS and new customer acquisition? The segment of customer’s auto identified by the recommender system of enalito can go on to become the segment which acts as a base for the advanced recommender system of Facebook and likes of them. People most similar to this input segment (look-alike) can be targeted in the social media campaigning. This generates much more specific narrow targeting compared to any other segmentation capabilities of such a social media platform used.
It is possible that out of 100 customers targeted by advertisement by segmentation approach only 1% are truly interested in buying them and end up buying them. If cost of every ad and action is 1$. Out of $100 spent in advertisement there is 1% conversion. If the cost of the product is $100 then such an ad will give $100 revenue for $100 spent in advertisement. On the other hand if look-alike segmentation on the base of ideal seed of interested customers is used, it is possible that the ad will reach to much more accurately narrowed down interested customer base of Face book. Which can result into 10% conversion rate. If this happens $100 in advertisement spent can result into $1000 in revenue. A ten folds increase in ROAS. If it is 10 times 5 times or much more can vary from case to case. But this far more superior approach of launching new products is surely the way to go towards the journey of a long term sustained growth in revenue and profits of the retail business.
A store where every product is so intelligent that it knows who wants to buy them from existing customer base. Chaining of the best recommender systems one of marketing and customer journey automation system of Enalito and second that of face book also enables every product to become so intelligent that out of those millions and more of all potential customers in the world. Every product knows exactly who wants to buy them in the whole world. Even on day one when the product is newly launched and born in this world. The utopia of a store with only the best sellers is possible only with such kind of intelligence.