In the previous article (Part I) on the self-serve model, I discussed where it works and where it does not, and why to use a self-serve model.

Self-service model risk

The self-serve model has its own risk. So before thinking of implementing that, it is important to evaluate the risk and its mitigation strategy:

1) Spreading too thin and losing track of the beachhead: A company that has grown by focusing on SMBs may end up spreading itself too thin in a pursuit of a self serve business model.

Mitigation strategy: Use a small team to validate the idea. Lean methodology can evaluate and create a much bigger opportunity without significantly diverting the resources

2) Complexity of the platform with the two models: Features team may be going in two different directions and launch competing product that dilutes experience for both sides of the user

Mitigation strategy:

• Define north star metric to orient the whole company towards a common goal
• Collaboration among both sides of the team to cross pollinate ideas and sense of ownership of both sides of the equation
• Leadership’s involvement to ensure team is marching in the right direction

3) Margin may get hurt while chasing volume

Mitigation strategy:

• Acknowledge the trade off and craft strategy around short term hit until economies of scale hits in to increase margin and unprecedented growth
• Evaluate the breakeven point where Marginal cost (MC) < Average Cost (AC)

4) Running after too many segments in a long tail may lead to catching none

Mitigation strategy:

• Identify the beachhead segment and create the best possible product to serve their needs. In the latter section, I have identified a segment that may serve as the beachhead segment.
• The narrower the definition of this segment the better the chances to capture them.
• Resolve friction in the product funnel before extending to other segments

5) Operational costs may be prohibitively high to justify acquiring the long-tail customer

Mitigation strategy:

• Run a pilot to evaluate the CLTV, Acquisition cost, and profit per customer
• A model with known knowns and known unknowns to take into account operational impact

How to evaluate whether self-serve model is the right thing for the business?

The following inputs is important to take an informed decision in entering this market: Customer, product, and Market and competitive landscape.

How to collect data before launching the product in the market?

a) Desk research (Gartner, Factset, etc.) — Market size, etc.

b) Qualitative insights — Ask your customers

• Define the problem that they are trying to solve or the value that they want to achieve such as reaching out to prospects with small gifts so that meeting non-show goes down
• How do users solve the problem in absence of a self-service platform — May reveal both direct and indirect competitors
• Use as simple as google doc to collect pain points

c) Plot customer journey map on how they achieve their goal today — This will help understand inefficiency and friction in the customer journey

d) Prioritization:

• Group pain points in themes
• Prioritize buckets — Use further interviews to prioritize them
• Identify solutions — How might we?
• Impact — Cost matrix

e) Packaging and Pricing — Value proposition and value captured

f) Mock and Focus groups

g) Prototyping and user interviews

h) MVP and user interviews

i) A/B Testing with feature rollouts

j) Build measure learn — Iterate, Iterate, Iterate

How do you measure the success of self serve MVP model?

Now that you have launched the MVP, Congratulations! It is important to measure the success both in short term and near terms. While it may change from business to business, a few metrics that we may track are:

North star metric: Short term (% New sign ups for self serve model) Long term — WAU (Weekly active users)

1. Acquisition:CPC, CTR, CPM, CPA, % New sign up (How many people come and how many of them sign up)
2. Activation: New account funded, Average account value, New campaign created
3. Engagement: Conversion rate at each stage in the funnel from acquisition to activation to retention to engagement (Discovery, Sends etc), New feature used such as % users who downloaded the detailed analytics, Contacts per user,
4. Retention: WAU/MAU Retention rate, Churn rate, Cohort analysis, Time series analysis, Renewal rate, Upgrades to higher value account
5. Monetization: $/Campaign = Average campaign value, $/User/Annum = Average Account value
6. GMV = Total sales dollar value for merchandise sold, Revenue, Profit, CLTV etc.
7. Referral: K factor, Participant CTR and conversion rate

What shall we do post MVP — what experiments shall guide the evolution of our strategy and in what order?

Once MVP is launched, idea is to observe and learn from how users are engaging with platform. This is the best time to detach yourself from the idea and enter the student mindset.

Few methodologies that I find really help to guide my product strategy post launch are:

a) Quantitative insights — Collect data around key metrics, will share the ‘what’ behind the usage

b) Qualitative insights — Recording of users are interacting with the landing page, CTAs etc. This will help understand the ‘Why’. User interviews of the self serve users to understand whether you are solving their core problems. I would use the feedback to inform the future iteration.

c) Generate hypothesis: The marriage of ‘What’ and ‘Why’ will define the hypothesis. Generate as many hypotheses as possible

d) Define, Prioritize, re-prioritize backlog as new data comes in, implement, launch, test, and measure: Small iterations to test each hypothesis and use that learning to inform the next version of experiment. I would avoid achieving local minima here such as CTA should be green vs red and instead will go for bigger picture.

e) Launch, learn, and iterate

f) Expand from one beachhead segment to the next

g) Experiment around following levers to discover 10x ideas

• Packaging: Keep identifying new value propositions, rephrasing the value propositions, and combinations of those value propositions to increase engagement and retention
• Personas — Which personas are doing better vs worse, why?
• Acquisition channel such as organic — Email and Notification, SEO and inorganic — advertisements/SEM
• Features: Launch and sunset features as users engage with various features. Feature churn is not bad, settling a little too soon when we are trying to refine our self-serve sales model will lead to inefficiencies as we later expand to different persons or user categories or geographies
• Pricing: Product-market fit is not set in stone but is in motion. Understanding price sensitivity of customers and identifying levers of price differentiation will help unlock unprecedented growth opportunities. As forms invest in identifying new personas, new niche market, verticals to cater to, we will have to evaluate pricing continuously. Experiment around Trails and freemium model will help understand what increases adoption and reduces churn.
• Stages in product funnel — such as experiments around improving the conversion rate where the drop-offs are maximum
• Conversion of self-serve vs high touch sales lead — Will provide valuable insights on how to optimize various parts of the self-service model

The order of carrying out these experiments should be guided by

Order: The order of experiments depends upon qualitative, quantitative analysis and also cost analysis but in general philosophy is:

1. Global maxima vs local maxima: A/B Testing can only get local maxima. Evaluate the overall strategy around which mountain to scale before scaling it. Focus on 10x opportunities before going for CTA changes or design changes that yield incremental value.
2. The cost-Impact of each experiment such as the impact of pricing is much bigger than small tweaks in packaging at times.

Self-serve mode is not a replacement for the high-touch model. What works for one business model may not work for others so before implementing a self-serve model, get to the basics. When the self-serve model stops working time to assess the product and customer requirements.

Hope this was helpful. I enjoy writing on growth and monetization. Feel free to connect on Linkedin. Read our other articles on Product Leadership, Product Growth, Pricing & Monetization strategy, and AI/ML here.

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