The customer is always right! But how does a business understand this?
We have heard the cliched but true "The customer is always right" phrase. The question however is: "How do you know what the consumer wants or thinks?”
In my opinion, the answer is dogmatic capture and analysis of consumer data collected throughout a customer's journey. But why do we need to understand the customer?
Equality + Opportunity + Technology = Higher Competition = Higher Need to Understand the Customer
Until the 80s, businesses around the world were broadly of two types:
Massive monopolies (Banking, Insurance, Manufacturing, Technology, Defence, Consumer Goods etc.) shielded by protective policies, that defined rather than followed customer expectations. The need, intention and the methods of "understanding the customer" at a mass level were pretty scarce and it was comparatively easy to bend the customer's will to the benefit of the company's bottom line.
Small businesses that had a limited set of consumers with a very high level of personalised connect and, hence, an anecdotal but comparatively accurate understanding of the consumer.
The concept of startups run by visionary, energetic teams taking on these behemoths and successfully defining industry standards, through innovative products and services was extremely rare, if not unheard of.
Rapidly evolving technology, shifting policy landscape in favour of an equitable market, increased intermingling of cultures / ideas from around the world has given rise to the era of disruption - where entrepreneurs, backed by deeper consumer insights, are either redefining or making entire industries redundant.
Competition is clearly on the rise and companies can no longer afford to slow down / become complacent. No sector is immune from disruption. Even the disruptors are getting disrupted within a span of five years.
Disrupting the Disruptors
Zerodha, the OG investment tech company in India, made it extremely easy and affordable for retail investors to trade on stock exchanges, pushing all existing platforms to the margins. But they primarily banked upon the seasoned (or highly motivated) investors more than on the comparatively clueless masses.
Groww identified this unmet customer need for "education" as a huge hindrance and went to task to solve it. As they packaged their marketing in the form of "educational content", Groww was also able to recognise the importance of an intuitive, easy to navigate interface, a smooth onboarding, and an effortless trading experience.
They did not possess any exceptional technology or processes. But the ability to capture and constantly act on consumer insights finally helped Groww's user base overtaking Zerodha's in 2023.
Consumer Data Capturing Strategy
Now that we understand the "why", let's understand the "where" and "what" of consumer data capturing. Every consumer goes through a typical journey when purchasing a product or a service.
Pre-Discovery ---- > Discovery / Awareness ----- > Interest ----- > Desire ----- > Action / Purchase ----- > Separation / Churn
To describe these in further detail, let's take an example of a startup trying to address the mental health crises by providing online counselling sessions.
Pre-Discovery: The customer is not even aware of your existence. He / she might be undergoing depression or anxiety but doesn't know even about your product.
Discovery / Awareness: The customer gets to know about the existence of your product either through an advertisement, word of mouth (which is very typical in this industry) or social media mentions etc.
Interest: The consumer comes to know about the features of your product - in this context - about pricing, access, therapy methods, expected outcomes etc.
Desire: The customer is kind of convinced that this is the product that he / she wants to purchase
Action: The customer enrolls in your mental health program by purchasing a subscription.
Separation / Churn: The customer tried your program for a week but did not attend any further sessions and did not renew their subscription.
Developing Insights at Every Stage = Continuous Success
To stand out at each of these stages, the company must provide an exceptional experience at all points – and this is where capturing data at each stage becomes immensely important.
It is also important to recognise that customers can be grouped into various types (cohorts) – based on some common criteria (example income, age, gender, etc). The assumption is that if A & B belong to the same cohort, they will most likely interact with the product differently than C & D who belong to another cohort.
So how would this mental health startup go about this process?
1. Pre-Discovery Stage
The aim is finding the most fruitful channels to enable consumers to "discover" this product.
Answering following questions through data can help:
What type of consumers are more prone to depression / anxiety which can be addressed by your product? Sample surveys can be conducted to verify these hypotheses.
What do consumers do before even trying to look for solutions? Example listening to sad songs, motivational movies etc.
What motivates the customers to look for solutions? Example coming across a celebrity speaking about mental health.
Where do consumers go to find solutions? Example online forums, YouTube videos, religious figures etc.
What are they looking for when they are searching? Example Are they looking for educational videos or speaking to a therapist or meditation assistance etc.
These would be open-ended sample studies, primarily using secondary sources of data.
2. Discovery
The aim is to understand the precise motivation / mindset of the consumer when they discover your product and how to make it most relevant to them. Find answers to the following questions:
Soft or hard discovery? Which works better - you forcing yourself into people's view by doing aggressive outreach or a subtle introduction through influencers / articles into the minds of potential consumers. Different approaches might work with different cohorts.
What is the message that different kind of customers want from a product like yours? Example someone going through the loss of a loved one would want a sympathetic tone of messaging while someone going through the loss of a job would want a more motivational aspect to the message.
3. Interest
The aim is to understand which features are the most sought after by different types (cohorts) of customers. This can be gauged by surveys, analysis of sales conversations to judge time spent or frequency of topics emerging in these conversations.
For this startup, these can be quality of therapists, price, session durations or the refund / return process in case things don't work out.
It is highly important to always tie these observations back to the customer cohorts and refine these journeys based on constant feedback.
Example consumers with prior therapy experience would be more interested in therapist quality compared to other features and their journey needs to be driven by this insight.
4. Desire
We want to know what sparks the desire to make a purchase, which nudges work for which customer cohort?
Does a on-spot freemium offering / EMI options / discount appeal more than an all-time low price / freemium offering?
Or showing success stories of a particular therapist nudge the customer towards making the purchase? These observations also need to be tied back to customer cohorts in order to make modifications to their journeys.
5. Action
What is the path of least resistance for the customer to subscribe for the product?
Time taken / mode of payment / ease of confirmation / telephonic support / post-action engagement are important areas where data needs to be captured for each customer cohort.
6. Separation / Churn
This is one of the most overlooked aspects of a customer journey. Brands spend a lot of time wondering what makes the customer click to increase their top line but many forget that loosing a customer who has already advanced in their purchase journey is far more damaging for the bottom line.
Understanding why customers are cancelling / churning through data is far more important today considering the multiplicity of options and the high customer acquisition costs.
Tech Helps, People Make it Real!
"If only we could solve this through Tech" is a common reservation that one comes across. There is merit to this concern since manually run processes tend to have gaps and errors. At the same time, it is far more fruitful to build tech after trying out multiple manual approaches and finding out the closest to optimal ones.
The key is for everyone in the organization to know WHY data is important and HOW each datapoint captured is contributing to the short and long-term success of the Organization.
Benefits of Unleashing Data
Data capturing in this fashion has huge multiplier effects, some of which are:
A deeper understanding of each customer cohort – allowing the marketing and design teams to come up with better product propositions and sales team to come up with better pitches.
Opening-up the journey to productization – reducing errors and improving efficiencies.
Ability to quickly rectify mistakes / strategies which are not yielding sufficient results.
Increasing the scope of objective comparison of business performance with time.
More visibility to decision makers upstream – allowing them to design business strategy that is in sync with changing market realities.
Application of advanced data technologies like AI / ML in multiple use-cases.
コメント