Starting Conditions\u00a0\u00a0\u00a0<\/strong><\/h5>\nOur customer in the retail industry had a main concern about shrinking revenue and market share, despite major efforts and investments in buying new traffic and nurturing existing customers.<\/p>\n
The following graphs show the development of KPIs in a time range of 2 years. The percentages in the small boxes show the average of the yearly comparison on a monthly basis. The color coding represents a rating of the development from green (no negative or positive development) to red (negative development).<\/em><\/p>\n
<\/p>\n
Looking at the numbers above, they had a reduction in revenue of 12% within one year. The cause was a combination of less orders and less average order value. The corrective actions (and related analyses) were very much focused on the usual: improving CTR and CPO performance of the targeted marketing channels, optimizing onsite CR, and analyzing product popularity to increase up-selling and cross-selling. The challenge was that at least two KPIs they looked at were showing no significant drop and improvement.<\/p>\n
<\/p>\n
Their efforts in new visitors and conversion rates seemed to be successful. Other typical marketing metrics were ok too. Onsite user behavior was fine. On the surface, everyone did a good job \u2013 but the most important KPI \u2013 revenue \u2013 was in free fall.<\/p>\n
Applying Customer Analytics\u00a0\u00a0 <\/strong><\/h5>\nAs a marketer or analyst, you need clear indications outside the usual range of fluctuation \u2013 proper actionable insights. When Mapp checked the customer development KPIs, we already had a suspicion but still were surprised by the results.<\/p>\n
First, we looked at the Customer Conversion Rate <\/em>which shows the relation between new customers and new visitors. The website was generating new visitors and we wanted to see what happened with them. A reduction by 13% was a first significant result. The (expensive) efforts in getting new visitors were obviously fizzling out, since they had no impact on the revenue \u2013 quite the opposite.<\/p>\n
<\/p>\n
The typical retailer does not generate profit out of one-time buyers. Two or even more orders are required to justify the marketing investment (Customer Acquisition Costs vs. Customer Lifetime Value). Therefore, our next step was to check that performance\u2026 and it got even worse.<\/p>\n
The Repeated Order Rate<\/em>shows the relation between the first and the second order (How many customers repeat purchase?). As you can see, within one year the share of new customers who bought again (and become profitable) sank by 17% and the time between orders went up by 23% \u2013 resulting in not only less orders but also in a negative trend from a long-term perspective.<\/p>\n
<\/p>\n
Since the main concern was the revenue, the next KPI we checked was the Order Value Increase<\/em>\u2013 the relation between the order values of the first and the second order. It showed a very negative development, the relation went from around 6%+ at the beginning of the 2-year period to nearly 0 at the end. This means while in the past customers generated 6% more revenue, if they bought again, they stagnated now with a strong tendency towards even reducing their basket size in returning orders. For a business which relies on customer loyalty this is an alarming signal.<\/p>\n
<\/p>\n