This week we are talking about the Retention Science business model and using the business model canvas to understand the different components of their model. For those of you who don’t know Retention Science is a marketing company that helps brands retain their customers. This is done through the use of Artificial Intelligence (AI) and machine learning to personalise every single customer’s experience of a brand so that churn is reduced as much as possible.
So who are Retention Science’s customers?
Let’s get the obvious points out of the way first. It’s AI so it needs lots of data – so it’s customers are internet based companies with a certain amount of scale. For the machine learning to work well we’re looking at 100k customers or more. Because the business model focuses on Retention (Hat tip to the AARRR model typically we’re looking at businesses with a regular recurring revenue model. That is normally going to be SaaS companies or companies selling subscription boxes. The customer they trumpet most is Dollar Shave club – known for it’s super cool videos (I’m still on Gillette [sigh] as no one in Malaysia has thought to clone that idea)
So we’re looking at CMO’s of data driven companies with traction who have got a powerful brand working but need to stop leaks in their retention. That is taking us onto the value proposition.
Retention Science’s value proposition is
We reduce your churn rate by personalising your customers experience in a way that cannot be done manually.”
Marketers have several problems here:
- Producing content is expensive and time consuming.
- Putting the content in front of the right segment is difficult.
- It’s almost impossible to do this other than as one off projects without massive resources.
What Retention Science does is to segment customers on their behaviour in real time and based on that behaviour show them the content that has the most success based on customers exhibiting similar behaviour in the past.
Retention Science don’t show their pricing on the website (suggesting that they are not going for the $50/month market) but it’s clear that something like this can automate the work of several marketers if they were hired to attempt to do the same thing. More importantly not only is the cost of hiring marketers to do this cost, but the system continues to get better and better over time and react to emerging patterns of behaviour. So there is a clear ROI case that can be made as you cut marketing heads and reduce churn to increase revenue.
Customer relationships are basically consultative selling. The company uses demos to qualify customers and there is likely to be some integration involved to connect the customer’s web platform and CRM through to the Retention Science’s servers via API. The customer relations can really be seen as a standard SaaS sales, account management and customer support model for larger organisations with little Face to Face time.
ReSci’s main channels are the web site, where there is extensive content marketing in place, and its partnership agreements with agencies. Many customers will be engaging in a larger project to improve website performance and for all its benefits ResSci is not a one stop solution. The one stop solution is delivered by agencies, technology vendors and design houses delivering projects for their customers. By partnering with them Retention Science is able to increase the value of their offering and get the partner to do much of the expensive face to face sales on their behalf.
The key activities for Retention Science are looking at customer data streams and looking for new products that can be sold off the back of them. What can be learnt from the data? How can this learning be transformed into a product? Does this product work and add value? The three current suites of products – email, website personalisation and subscriptions have all been developed in this way.
The other key activity will be the integration and implementation – making sure that customers can be set up quickly – allowing a generic platform to be customised to the client requirements as quickly as possible. This is again likley to be very much a coding activity.
Now that it has been up and running for a few years the key resources in the Resolution Science business model are the data that is has from multiple sources and the IP contained in its algorithms. That’s not easily or quickly copyable by competitors. Lesser resources will be the human capital but whilst this is important AI skills are becoming more fungible as industry grows in size.
Agencies and technology vendors are key partners as will be the cloud service provider that Resolution Science users. Finally it’s worth considering the big data/AI community as a partner given the huge reliance on Hadoop and other big data tools
Product development and design is going to be the largest single cost with a rented IT infrastructure coming in second. The third main cost will be the SaaS sales team. Sales is often a larger component but this seems to be due to relative ease of hiring technical sales as opposed to engineers in Santa Monica. Finally there will also be sales commissions to it’s partners.
Revenue is based on a three year contract implying large front end set up costs which may or may not be clawed back from the customer at startup. Otherwise the focus is on pure recurring revenue for each of the different products
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