Case study: flowers store chain growing revenue

This week I had a talk with prospective client and make a proposal which should increase her sales and new customers on ~25% based on data we had. Below are ideas and methodology of the implementation.

The idea

Based on my data (Russian market, 2019) the market of online purchases is quite big (~50% from all market) and continues to grow, ~30% of customers purchase goods over mobile devices and their market share continues to grow. It becomes crucial to have digital sales channel both on desktop and mobile sides where customer can view goods in one click, in two clicks - make an order, in three clicks - pay for it.

In case of my potential customer she has few stores which sells flowers. Here is the top 3 reasons to implement it.

Firstly, it seems to be the fastest way to make an order: mobile cell phone is always in a pocket, application from the company I trust is already installed and I can make an order in 3 clicks.

Secondly, it could motivate a client to make an order. You are familiar with SMS notifications and notifications from others applications. Our mobile app also could send a notification a day before event to remind a client he or she might want to buy a bucket of flowers (8th of March, 14th of February, Day of Mother, 1st of September, 31th of December etc)

Finally, it can give you an excellent advertisment. Let's say the day after receiving flowers we ask a happy customer to write a postiive recommendation into the social network and as an appreciation for feedback we give him or her a 15% discount on the next order.

How is it interesting for business owner? How to analyse it?

Discovery phase

Despite on a great power of analytics only response from end users will give the most complete understanding of how right you are. In this case a talk with business owner or her representative was a next vital step in the disovery phase.

What I was looking for is key business metrics of her business, plans for growth and current issues. For key business metrics I use key metrics from unit-economy: user acquisition cost (CAC), customer lifetime value (CLV) and conversion rate (C1)

During our talk and interview I got answers on the many of my questions, some was left unresolved because some business data just is not recorded, also another valuable extra information was popped-up.

Without mentioning exact numbers I got understanding about average purchase receipt, average amount of purchase receipt for average month, monthly expenses on marketing, percent of customers attracted by this marketing efforts and conclusion the majority of customers came naturally, without expenditures on marketing. Big percentage of customers comes online, from social networks. Conversion rate was not available and I offered general 1 from 10.

Extra information was referred to issues business is faced at this moment. It was reaching plateau in obtaining new customers as the most of the current customers are the same people who returns to the shop again. Another issue was many clients are not tech-savvy or feels reluctant to install new app even if it promise them to give benefits cashback system (customers complains about lack of storage space on their devices).

The last interesting point from our talk was cashback system is used by her business. This cashback system is just a part of one interesting product. This product also offers a mobile app which out of the box covers 2 of 3 features I mentioned at the begining. It seems to be there is no reason to implement custom app, however there is still the reason to go with custom solution.

This product has one drawback which might be crucial. It is designed for business in general and covers variety of different businesses. It means unification of business processes, it means ignoring some unique aspects of each business, it means ignoring valuable information from local market. All of this might be reasons behind your success of failure. That's why implementing custom mobile app is still a valuable option.

The analysis

The first question we have to answer is regarding reaching plateau in acquiring new customers. Does it mean there is no space for growth because business reached the majority of the market? (this chain is not in the big city). It is an issue to find out data for the local market, but general analytics should fit too. If we trust this data from this source, then we should have a good capacity for growth.

The next question is how does mobile app impact business metrics? How does it solve the issue with acquiring new customers and which gains it bring?

What is important for us is our monthly burn rate (gross) - expenditures on acquiring new customers including fixed costs to run the business, - should be less rather than revenue customer brought after the first and next orders.

Let's calculate the customer acquisition cost (CAC). It is done by dividing our monthly customers on monthly budget on the marketing. We got the value X which is our customer acquisition cost. However, business owner said only part from monthly users came due marketing activities which means it should be included in our calculation. Now it looks as "our monthly customers" * "percent of customers gained due marketing" / "monthly budget on marketing" which gives our CAC. Furthermore, we based our calculations on users who made an order which should be only part from amount of people who noticed the advertisement. Early we agreed on conversion rate in 10%. The final formula to calculate the CAC is (("our monthly customers" * "percent of customers gained due marketing") / "conversion rate (0.1)") / "monthly budget on the marketing".

Let's calculate now revenue from new customers we expect. It just multiplication of the "average purchase receipt" on the "amount of new customers". From our data above near half of client's target audience wants to make purchases online, near third of them wants to do this via mobile devices, total customer base reached by marketing activities was evaluated too. It should be 25% customers increase compared to current client's amount of customers. By multiplying this new customers on average purchase receipt we get information about expected revenue after running the mobile app.

Viral effect from recommendations from mobile app in social networks should be explored in another topic.

By having data about others expenses for running business, we could evaluate the profit that should be brought by launching mobile app and consequently return on investment in development.

The conclusion

This model was built on data from business of my prospective client, from analytics of trusted sources, and part of data were assumed based on experience. The more accurate the data, the more effective the forecast and the quality of the decisions made. This is one of the reasons why development of software products often done by iterations (~2 weeks) - it is necessary to validate the formulated hypothesis as soon as possible with minimal investment.