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Case Study: How Analytics Can Make Your Customers Stay With You

In this day of age, businesses need to consistently make decisions to thrive and grow in order to succeed. However, all a customer needs to do is to tap their mobile screens a few times and they would be exposed to tons of businesses that offer the same services or products as we do! Without a doubt tons of questions as below would pop up:

  • How can I attract the right customers?

  • How can I boost my sales?

  • How can I make my customers stick to my business?


If these questions sound familiar to you, then there is one option that you can consider to resolve all your questions and problems - Data Analytics! We know that data is important to us in this modern age and the tools we can use to collect data. However, if we don’t analyze that data, it’s going to end up like that coupon/ voucher in our drawers that expires when we finally remember about them!


Taking my experience working in a retail sector previously, despite being an executive, we decided to collect data of our products and services and did a simple preliminary analysis of preferred product type, color and price. The results? We manage to consistently increase our total area sales by at least 10% every month for a period of 4 months. Data is truly at our fingertips, but the million dollar question to ask ourselves would be - Are we using these data to our advantage or just leaving them to expire in our hands? Let’s take a look at the following case study to understand how data analytics can ultimately make our customers stay with us!



Case Study 1: Attracting The Right Customers

Jenny currently runs a business that provides baking products, tools as well as confectionery goods. She often chats with her customers and remembers her customers enjoy promotions, sales and events. As such her business participates in online sales events such as 11.11 as well as year-end sales and clearance sales. Furthermore, they often organize events such as baking competitions and live bake offs. However, most of the events they launched had little to no participants and online sales did not perform as well as they had initially expected.


How could we help Jenny? What data can we use to find the right strategies?


If we take Jenny’s customers’ demographic data (as below), what marketing strategies would you recommend?

Details

Customer Age Range

25 - 55 years old

Gender

85% Female, 15% Male

Customer profession

Housewives, restaurant owners, confectionery stores



Based on her customer base from the table above, Jenny could potentially put more effort in marketing to restaurant and confectionery store owners. For example, she could send out promotional campaigns before major celebratory events or major festivals to attract restaurant owners and confectionery stores. Additionally, she could also invite baking instructors to conduct a session for housewives to attend and not only learn how to bake a cake step by step, but to also decorate it and bring it back home for their families!


From the case study above, using data and analytics will allow us to better craft effective marketing strategies to accurately target both existing and potential customers for our businesses.



Case Study 2: Boosting Sales


Using my previous experience as a case study, let’s introduce store A and B. These stores are all located in the same area and have been facing difficulties in terms of sales. They were adamant about making sure they are well stocked to prevent losing out to other businesses. Thus, they would often order equal amounts of stock from us to avoid running out of stock. The table below shows some data with regards to the two stores:

Store A

Store B

Stocks from the beginning of the month

Product 1: 50 Product 2: 50

Product 1: 50

Product 2: 50


Stocks at the end of the month

Product 1: 30

Product 2: 16


Product 1: 11

Product 2: 35


Best selling color

Silver, Blue

Black

Accessories offered

Smart watch, Powerbank, phone casing

Earbuds, powerbank, phone casing

* Product 1: RM 1199, Product 2: RM 2299


From the table above we can identify ways to help boost the sales of both stores! Store A customers prefer to purchase more high-end premium products whereas Store B customers prefer to purchase mid-end economic products. From this, we can clearly identify which product can be focused for which stores when they order stocks! Not only does this prevent issues such as hard to clear stocks, this also ensures greater ROI based on the businesses investments! Secondly, we can clearly identify customer preferences based on their preferred colors and can focus on those options instead. Customers would feel understood and businesses can earn money!


Before we move on to the third case study, we can actually utilize one more data from the table above, hot selling accessories! By understanding our customers preferred accessories, we can use this chance to utilize combo packs or add on deals! Average cost of a power bank would be RM 150 and a casing would be RM 15. We can combo these together and sell to customers with the price of RM 1300 for product 1, casing and power bank. This would effectively increase customer average spending in the store from RM 1199 to RM 1300.

As such, data and analytics allow us to understand our products and services better to effectively apply suitable strategies in boosting our businesses sales with tactics such as combo sales and hot selling products/ services.

Case Study 3: Making My Customers Stick To My Business

Simon is the owner of a large beauty product e-commerce platform. He offers a variety of products to multiple customers and below we have a three sample profiles for his customers:

Customer A

Customer B

Customer C

Average spending per transaction

RM 500

RM 1500

RM 150

Frequency of purchase

Once every 3 months

Once every 6 months

Once every 2 months

Recent Purchases

Skin care products

Fragrances, skin care products

Cosmetics


From the table above, we can identify aspects in which Simon could focus on to provide a better shopping experience for his customers. Taking average spending as an example, Simon could provide vouchers according to the average customer spending to encourage more spending while also allowing customers to feel rewarded when they purchase from Simon as they need not spend huge amounts of money to get discounts or vouchers. Furthermore, Simon could reward customers such as customer A and customer B with exclusive premium member benefits to encourage them to continuously purchase from his store.


Secondly, Simon could utilize the frequency of purchases as a way to estimate when his customers would make a purchase next. This would allow him to prepare follow up messages as well as additional promotions to nudge customers to purchase. This will allow customers to feel that the services provided are very personalized and tailored according to their purchases. Thirdly, Simon could utilize customers' recent purchases as data to suggest other products that the customers might be interested in! Take my experience recently with SHOPee for example, I realized my average spending increased recently as I find myself looking at the add on deals as well as purchasing more products as the shop offers vouchers according to amount spent while offering vouchers via SHOPee messages.


By utilizing data, not only will we be able to effectively determine marketing strategies and sales boosting strategies, it will also provide us insights on our customers and the methods we could potentially use to build our customer base through vouchers, rewards and other benefits!


I believe all this talk about analytics has made you want to start using it for your business! Not sure how to start, where to start or who to seek for help? Fret not as we are able to provide you with the necessary tools and services to assist you in analytics! Interested?


Check out our solutions to take an in-depth look at how your business can utilize analytics today!