How Retail Stores Can Use Machine Learning To Boost Their Sales?
Foot prints create a pattern by which consumers shop. Creating demand for the product is important as the pricing is directly tied to this factor. Pricing and coupons influence consumer to buy more products. If you look at the big data, there is timeline in which consumer makes these purchases. They either shop weekly or Monthly. Consumable products get more reorders than other products. Last thing a customer wants is a empty shelf or Product not available at the display if you’re a e-commerce site.
Identify the product combination pattern, Find Out ways to bundle the product up next time. It’s possible if you reduce the product cost by one fourth as a discount, there are high chances the consumer would buy two products instead of one. If the offer is saving people time and bringing them value for the money, they will take it.
Identify Peak And non peak hours from the big data, redirect resources to better serve consumer.
Based on the buying pattern, market the product and offers wisely to reach the consumer who could potentially buy your product. For example, you find that from the big data that Saturday and Sunday are the best days, since more customers buy your product. Marketing to the right demographic is important to convert the potential customer into a regular buyer. Add limited period offers, Coupons, discounts during these days.
Events, Gaming Attraction, Political events often attract consumers, figure out the special days in the month and target audience using the hash tags on social media. Connect with your customers on social media!!!
Every customer is reachable on social media. Main aspect is, you find the consumer preference on social media. Send goodies they like to frequent buyers. Personalizing is the way to go in the future. Once you have the consumer data, send appropriate offers to make them reorder.
Product Recommender System
This is gaining momentum among eCommerce sites. Consumer behavior has to be grouped to recommend appropriate products. These are polite way to up-sell products to consumer. It can be even more efficient if it’s coupled with coupon recommended system. Machine learning system can predict the most bought coupon and product so that they can be reused with new consumer. There are ton of pointers in this section. There will be a separate posts to discuss the beneficial nature of this system.
Pricing is one of the major factors influencing consumer behavior. On the flip-side, it’s one of the factors to worry about. Retail stores would like to have a stable pricing instead of a fluctuating one. Machine learning regression models can track price of goods. Retail stores should focus on new technologies to reduce the sourcing cost, marketing and logistic cost. Amazon Alexa, Google Home are next media platform where consumers will be available. Every car and home would have voice enabled technology, it saves time. Marketing in white space is important to finding new customers as well as the cost variable.
Retail stores need to start pod-casting to reach more customers, it’s Free!! Facebook ads are incredibly under-priced. Combine the marketing campaign with machine learning and big data, it’s sure recipe for next level.
Consumers act based on their immediate needs. With this being said, it’s difficult to track consumer behavior as their patterns change often. Machine learning models can recommend products based on their previous purchase as well as from their taste and likes. Big data is useful if it’s analyzed and put into proper use. Artificial Intelligence is the right tool for the job.
Without retail analytics, they could loss of potential sale because the inventory wasn’t managed properly or logistic delay or pricing or competition etc. AI models can forecast sales, demand and pricing for the product. This allows reallocation of resources like humans and capital. How much of sales is lost since the price point on the product was high? How much sale is lost due to lack of inventory or delayed logistic supply? How were marketing opportunities missed?
While there may be many variables to this equation but keeping track of it difficult. Retail stores need to go wide to increase foot prints to the store. Creating demand is one way to keep the cost of product low. It’s a big equation to solve.
Uber’s business model is predicated on creating constant demand with lower pricing fares. Model is only sustainable with demand. Uber sells time for people.
Consumer behavior can be influenced with pricing models and one such thing is coupons. Coupons come with expiry date, thereby influencing consumers to take action. People often reallocate resources to pay for their immediate need. If the coupon is saving time and money for the consumer, they will buy more with coupon. Lower pricing makes the product more attractive, as the people who thought it’s beyond their budget would buy in. AI models could create personalized coupons for customers so that they reorder.
If Machine learning is used in Inventory management, pricing models, coupons, product recommenders, logistics, sales and marketing. It creates stability in the business allowing the business to scale. Subscription models can give price protection for consumers and allow stores to raise capital.
Based on consumer big data, retail stores can track products that are reorder constantly, group customers that reorder frequently to send better offers for them. It also tracks the reason for the reorder of the product and make a timeline for the product reorder whether its 7-day period or bi- monthly or Monthly. This helps in inventory management and giving a timeline for the logistics to deliver the product.
These methods are practically applicable to many other industries like clothing, BPO, Sales, Marketing, Customer Care and many more. The possibilities are endless.
Logistics can have lower time delivery for product since we have the big data to know when the consumer foot print is going to peak every week. Logistics can be placed to deliver in places of higher demand, since we know the consumer buying pattern, time and days when the purchase peaks.