There are no two words closely linked in today’s business scenario than ‘retail’ and ‘ecommerce’. With a wide variety of choices from all over the world at your fingertip coming to your doorstep, online shopping has cut out a niche for itself. What was earlier considered untrustworthy and impractical is now the most popular trading platform.
With a growing internet population, customers expect more from virtual shops. Since customer satisfaction is the cornerstone for a growth in eCommerce, understanding your client base is extremely important. ‘Personalized’ products and services attract a huge sales volume and ‘individual attention’ is the key.
But how many of you actually know what your customer wants? A lot of ‘ayes’ if you have a physical store but for a virtual audience, it’s like staring at a blank wall. Can you figure out how long visitors stay on your page and what segment of your website they find most interesting? What time do most sales happen and from which region, gender and age group? Which marketing channels bring you the highest ROI? How easier would it be if you can predict what a customer wants after buying a particular item and categorize loyal customers from those you need to work on. What if you can compare competitor prices regularly and adjust yours to drive in more sales. No, it’s not impossible. It has been done, it is happening and is indeed giving astounding outcomes. It is called Retail Analytics and will revolutionize sales and market value for you. Trust the Thinker to do all what you need!
If you are thinking about a fail-safe strategy to accurately identify customer buying patterns, customer segmentation is what you should know about. In fact, it is considered as the building block of retail analytics. It uses RFM Analysis to identify characteristic traits of your customers.
Like the term RFM when expanded, the algorithm uses recency, frequency, and monetary values to segregate each of your customers. A person who often shops from you on a regular basis above a particular sum of money will be under one category while one who seldom shops more than a certain amount is classified under another. This way, you can ascertain the number of categories where you need to focus your marketing skills on instead of performing the same tried and tested tactics on all sorts of customers, especially those who don’t need them either way.
Of course recommender systems are pace changers when it comes to eCommerce. After all, the ‘want’ factor is the soil in which retail shopping has its roots; necessity is mostly never the case. Recommender systems thrive on that section of the brain which creates a need for a ‘might want’ objects. Romantic dating apps like Tinder solely work on the concepts behind these systems.
Recommender engines try to predict a customer’s behavioral tendencies based on the ‘preference’ or ‘rating’ given to a product. Data scientists here recommend mainly four algorithms to bring about accurate results:
One of the first data mining algorithms, it is used to identify combinations of products that frequently co-occur in transactions. A classic example would be of a store that discovered in its analysis that male customers who bought diapers often bought beer along with it. When the beer cooler was moved next to the diaper rack, the sale of beer increased dramatically. With the information from this analysis, a retailer can effectively organise two things: one, store layout and two, marketing(eg. Target customers who tend to buy shampoo with discount on conditioners and causing them to spend more).
Another feature that sets Apriori apart from its contemporaries is its computation speed. It is very fast and works well with a large number of products and even more customers. Apriori algorithm is thus suited for general eCommerce analysis and is fuelling recommenders for popular websites such as Amazon, eBay, Google, etc.
Market Basket Analysis
Similar to Apriori, Market basket also studies association properties of products. Though it is an accurate affinity analysis method, it is computationally slower compared to Apriori and can be used only for lesser number of products.
User based Collaborative Filtering
While the above mentioned algorithms are combination-centric, collaborative filtering is user-centric. Apriori and Market Basket evaluate the frequency of items purchased together by a user across multiple users. On the other hand, user based CF provide recommendations on the basis of user history. If a customer had bought eggs and butter, Apriori will show flour every time based on general trend analysis but CF recommends an item which you might have bought in the past. Hence, CF is more personalized than Associated filtering: the former focuses on ‘who you are’ while the latter is all about ‘what you are doing now’.
Item based Collaborative Filtering
In the case of a typical user-based CF, the algorithm works by finding a neighbourhood of users that share similar tastes with you and then giving out suggestions. But Item-based CF makes recommendations by discerning similar items to those items you had shown preference for. This helps to resolve the issue of sparsity in user-based CF when it comes to items such as books, CDs, etc. where on an average, only 1% of the total quantity can be bought yearly by any individual. In such cases, ratings are scarce and hence the results become inaccurate. Item-based CF examines user-item relations and computes a prediction score for each user-item pair.
It is obvious that each algorithm by its own is imperfect to be used completely in a recommender engine. Thinker’s Retail suggests a hybrid recommender system taking inputs from each of the above techniques and leveraging the results based on the vertical where it is to be implemented.
Isn’t it amazing how people who share common interests, age, gender or marital status often want the same things? Boys in their teens are often gadget freaks and young girls look for clothes and cosmetics. Married women are inclined to make their shopping more domestic oriented compared to unmarried women. Men in their 40’s often like to play golf or meddle with tool kits in their spare time and tend to buy these online. Of course these are only general trends. But understanding the mind of common people already takes you one step closer to building a connection with them. This is why you need demographic segmentation of your customers as well as prospects.
Now, the aforementioned methods are fundamental to retail analytics. However, in order to extract valuable information from all available channels and refine marketing tactics, the existing computational practices just wouldn’t suffice. The Thinker’s Retail has other proprietary algorithms to account for these crucial features below that can further filter products and customers.
Yes, customers have been classified, recommenders have started their jobs and items rearranged. So what is left to do? Time to evaluate your service! Don’t you want to know if all your marketing, discounts, offers and customer understanding has worked out or not? Using Thinker’s 3-point Slope Analysis, you can easily verify the validity of customer behaviour deductions and supporting sales tactics. For example, if a customer’s response to the market changes, the offers given to him can be monitored and modified. An analysis of that extent will help divide your customer base into categories which will further aid you in target marketing a group rather than perform a tedious individual scrutiny.
Product Market Fit
A product segregation technique, product market fit measures the response to products old and new, offers or no offers, market changes or not. This helps in deducing the best products for a particular market condition. Thinker’s Popularity Inference Factor helps classify products based on their demand. Popular products needn’t be put for sale but can be discounted based on demand. Likewise for unpopular products, specific marketing strategies can be devised to promote them. Thus, an appropriate pricing can be defined for every item you provide.
Monetary Sensitivity Analysis
Broadly speaking, customers availing products and services through ECommerce fall into two main categories: price sensitive and insensitive. The sensitive ones often surface only when they need a particular item or when there is a sale. The insensitive category has eyes only for the product. Either type of consumers needn’t consistently buy from you. However, our goal is to stabilise cash inflow so that it doesn’t fall below a fair minimum at any point of time. Reaching out to both these categories via different paths can always help to maximize profits and offer monetary security year round. The Price Affinity algorithm helps to sort out your client base in this regard.
As a retailer, displaying the right brands on your ECommerce platform defines who you are. Often, surveys have indicated that customers are instantly attracted to small or medium sized retail platforms for the importance they give to their products. This refinement can be in terms of quality, service, portraying the right brands or a combination of these factors. Now, brand analysis or filtering right brands for the right commodity requirement is performed by the Thinker’s Brand Factoring analysis. Hosting only the perfect brands bring momentum to the marketing strategies devised and helps you balance the art of selling online.
Price Point Analysis
A company’s competitors are rightfully called its greatest strength. And rightly so because there is always an opportunity to convert their setbacks into your strengths. This can be used to maximize ROI as well. A lot of information can be obtained when probing competitor websites. For example, your rival website might put up a particular price for a seasonal product during its off season. If you can reduce the price of the same object by a considerable margin, chances are that customers searching for that product will prefer you over your rival. With an increase in sales, the initial discount margin gets nullified as well. The data required for price tuning is exactly what this segment provides.
Just like the theory that human-to-human interaction will never die out, consequently direct marketing will never go into oblivion. People do ignore email blasts but promotional letters convey a different emotion. Sending discount coupons along with hard mails will result in new customers or help revive those who haven’t bought from you in some time. The Custom Ranker algorithm will help you categorize customers, products and their prices for this particular activity.
The ‘out of stock’ sign is something that discourages customers coming back for it a second time, especially if it is available on a rival platform. On the other hand, an excess of products which don’t sell over a span of time is unfavourable for retailers. Prediction being the core of analytics, demand forecasting helps maintain an ‘always in stock but never in excess’ stock position. Thinker’s Product Marker studies the market and predicts user demands based on sales reports from previous years, seasonal requirements, current events, etc.
Weather on Retail
Prediction can happen in many a way. Imagine the clear sunny day the weather reporter promised turning to a rainy day and you do not have an umbrella. Cursing the reporter and trudging to work in dripping clothes is a horror by itself. Preparing for major weather impacts is something everyone does promptly when a hurricane or earthquake alarm is raised. The MeteoWatch application continuously monitors weather reports and predicts weather changes. Putting up weather preparedness kits on your site weeks before a calamity occurs projects your humanitarian side in addition to spiking sales figures.
One reason why people prefer online shopping is because it reaches your doorstep from anywhere around the world. And the company which reaches them the quickest is always the first preference. For that to happen, planning the delivery route is of utmost importance. Thinker’s PathRouter app efficiently plans delivery processes, right from shipping to inter-city delivery routes. Not only does it makes you popular, but reduces your travel expenses by a significant amount. And remember, free delivery secures your position at the top.
Social Media Analytics
Trust social media to provide a quantum of feedback anytime. Now that people have a space to put in their emotions and experiences, you can tap the most out of this medium for customer feedback as well as marketing. The Emotion Analyser scans social media sites such as Facebook, Twitter, Pinterest, etc. for mentions about your company, products and services. It gives out three fundamental aspects of a review: emotions, basic sentiment and perspective. To get hold of those crucial reviews, the automated Emotion Analyzer categorises comments under designated ‘tokens’ so that you get a thorough understanding of the key areas where you need to work on.
Human resource is any company’s true asset. Evaluating them constantly can actually save a significant part of your investment. For example, tending to talented employees and training them for higher positions will make them eager to work with you rather than hop around. You’ll end up with loyal and hardworking workforce and save yourself from the extra cost of training new people to replace them. Thinker’s V4Workforce monitors your employees and provides reports based on their expertise, talent and day to day activities. It offers suggestions on adding new departments, predict future technological needs, optimize the organization’s structure and predict the probability of an employee’s success.
Any economy is rife with fraudulent activities. Hence, a fraud detection system should be in place for ECommerce as well. The two main types of fraud occurring in ECommerce are:
- Vendor sham: We all know that person who cribs about online shopping because they got a soap bar instead of the brand new mobile phone they expected or the cheap fake leather bag they paid an arm and leg for. Verifying vendor genuinity is necessary for customers to develop trust in you.
- Inventory fraud: Often, retailers find a mismatch in the measure of product inflow and outflow. Tracking this is a troublesome task by itself and something that is unwillingly avoided unless a serious theft occurs.
The Demarauding Analysis takes up these major frauds and monitors others such as payment frauds and customer shams. Thus, you can remove that feeling of insecurity associated with online shopping, both from customers and within yourself.