The HBR issue (Jan-Feb 2020) spotlights “The Loyalty Economy” which makes for a great read for anyone interested in business growth. This three-article series eloquently illustrates how the myopic quarterly investor reporting has stunted long-term growth for many businesses. At the same time, true leaders like Amazon’s Jeff Bezos, Costco’s Jim Sinegal, and Vanguard’s Jack Brennan focused on customers and had the last laugh. In the first article, Rob Markey of Bain recommends three important auditable metrics that reflect a healthy growth of customer value viz.
- The number of gross new customers acquired during the reporting period and the number of net new customers remaining at period end.
- The number of existing (tenured) customers
- revenue per new and existing customers
This aligns perfectly with our experience in measuring an outcome-based digital transformation deal that we did for a large multi-billion dollar division of a CPG major. We have demonstrated results of 5-7% growth in topline revenues, 60-75% call deflection, and 97% satisfied customers while simultaneously building a greenfield B2B eCommerce system and upgrading the call center technologies.
What customer measurements helped us achieve 5-7% growth in revenues and 97% satisfied customers?
As part of this transformation, we very closely monitored
- the total number of customers ordering in the reporting period,
- the number of customers acquired in that period
- the number of customers who have NOT ordered in the last 3 reporting periods
- the number of customers who have taken to ordering online
- the number of clicks /calls it took to place an order
- Customer feedback on the online system every quarter
with a double click into each of the above numbers by segments. We further diligently reviewed the trends of these numbers accounting for seasonality based on prior year history and adjusting for customers who declared themselves seasonal. Let us double click on the actionable metrics from this list.
Customers acquired in that period :
We compared this metric, period-over-period, and analyzed the campaigns that contributed to the growth. We further broke these numbers down by customer type and geography to learn what was going well from growth segments and campaigns to apply them broadly.
Customers who have NOT ordered in the last 3 reporting periods:
This was a lagging indicator of attrition. However, any corrective action taken 3 periods would be too late, by which time the customers had already settled in with the competitor. So we used this metric as a benchmark, but the actionable list was created by using analytics (and later ML) to come up with better predictors of who is most likely to leave, based on prior trends. For example, we noticed a pattern that customers who were leaving typically would stop ordering a certain product category and that became our early warning to reach out to them.
Customers who have taken to ordering online:
We ran campaigns to encourage the customers currently ordering over the phone to go online instead. While we debated offering incentives (such as “get $x off your nth online order”), after comparing notes with the online leader in the foodservice industry, we decided against it as the 24×7 online experience itself was the incentive to migrate. And we stopped calling the customers for orders in select markets as an experiment and the numbers proved to us that there was no drop in customers or revenues. That satisfied the critics within the leadership who did not want agents to stop calling, and we saw the adoption increase to tens of thousands of customers.
Clicks to order :
This was a B2B scenario where the majority of customers knew exactly what they want when they come back for their weekly/fortnightly orders, and hence this metric was vital to understand the friction in the process. Our effort was to make the process as quick and frictionless as possible for these customers, who, we learned from our route rides that they don’t have much time to spend on ordering, and they were also suffering from campaign /sales fatigue and hence just wanted to get their order in. Our UX design resulted in 60% of the orders being placed in 3 clicks (screens), and over 75% of the orders being placed in under 5 clicks. Our UX design to accomplish the pre-population of the cart for these customers using analytics and ML is a topic for another post.
Customer feedback loop :
We believe one of the key enablers for our success was the closed-loop feedback mechanism that we created within the system. After each major website upgrade release (typically once a quarter), we presented a short feedback-form after the order confirmation screen that asked customers to rate their ordering experience 1 (Bad)-5 (Excellent) and monitored it diligently. We kept it very simple for them to just 3 questions (how easy was it to find products, how easy was the ordering process, and how do they rate the overall experience?) where they can select 1-5 and a free format text box to add any other feedback. To address customer dissatisfaction promptly, all feedback with a 1-3 rating was automatically raised as a ticket to an agent. This resulted in a call back ASAP from an agent to inquire about their feedback and disposition it accordingly in a timely fashion. This, we believe, helped matters significantly in a) redressing the grievance of an unhappy customer before they decide to start looking elsewhere and b) letting us know in real-time the issues bothering customers so we could apply systemic fixes/ provide better educational material/ train agents accordingly. There were several other techniques that helped us accomplish these results, some of which are further described here.
In summary, the key actions we took after periodically analyzing the customer metrics and insights were to:
- Establish a feedback loop to have a human agent reach out and address all reports from dissatisfied customers within the next day or two while the issue is fresh and has not caused long term impact.
- Update help documentation, on-screen prompts, or videos
- adjust the campaigns
- change the SOP of agents as needed
- adjust the requirements of our quarterly releases
- create /adjust automated alerts to operators and agents, triggered by monitoring specific trends.
tvprasad 