Case Study 1: Deep Dive Helps Save $120M Account
- Don't Stop at the Top When Analyzing Loyalty: Deep Dive Helps Save $120M Account
- Client: Software and business service provider.
- Business Objective: Improve relationships with key accounts representing $600 million in revenue.
- Research Methodology: Online survey of multiple representatives from each account’s buying unit.
- Synopsis: On the surface, things at the largest account (120M) looked good. Average likelihood to recommend was similar – and strong – across accounts. What our client did not realize was that these averages masked important patterns and variations within respondents from the same buying unit.
- For this largest account, we found that likelihood to recommend declined as seniority increased:
- Our survey also measured importance and client performance on 13 attributes. Comparing these detailed ratings on perceptual (value) maps allowed us to explain this decline. We found that lower level respondents cared more about the product/service itself, where our client was stronger, while higher level respondents cared more about the business relationship, where our client was weaker.
In particular, the most important decision-maker (CIO) was unhappy about product inflexibility, lack of innovation, and especially time to market, putting the whole account at risk. This is illustrated in the map shown below. Our client was already having regular meetings with this individual, but it was not until we presented our results that resources were mobilized to proactively address these specific issues.
Economic Decision Maker Value Map
- Business Outcomes: Our client made an acquisition to supplement their technology platform, which decreased time to market and increased the flexibility of their software solution. In conjunction with proactive account management, customer perceptions are being changed and customer retention is rising. Topline revenue has increased and our client currently outperforms the S&P 500.