How to take a brand equity approach to analyst value

If you can’t value analyst relations ‘bottom-up’ (through sales tracking and win-loss analysis) then can it be done ‘top-down’ (using brand equity approaches)?

There’s no question about which of these approaches gets the most short-term leverage. It’s the bottom-up approach, where we add up the dollar impact of analysts on our sales pipeline. Showing analyst impact on sales gives a double boost to AR. First, it shows the short-term contribution of leads to sales. Secondly, also it increases the legitimacy of our marketing communications colleagues because it’s often including tracing leads generated by analyst comments as they flow through the pipeline. The good fortune for AR is that both positive and negative analyst impact strengthens the case for AR. If you see positive analysts creating sales, then that justifies the investment in AR. For high-value sales, one major client can just justify a decade’s investment in AR.

However, the bottom-up approach misses most of the impact. Analysts aren’t only involved in helping operators and enterprise IT buyers to make selection choices. They are advising buyers, sellers, channel partners, regulators, investors, legislators, thought leaders and even consumers. Consequently, analysts don’t only influence the buyer’s choice between rival providers. They bless certain markets with more favourable expectations, benefitting all buyers and sellers in the market.

The correlation can be proven statistically, even with methods as simple as comparing analysts’ mentions of vendors with vendors’ financial performance. Pretty much any attempt to make that comparison with time-series data shows not just correlation, but anticipation. Analysts’ advocacy for vendors improved in advance of those vendor’s financial results. This analysis is especially powerful with public companies, where the data are not only more easily obtained but reflect more stable accounting assumptions. A change in analyst perception this quarter is a great guide to the change in financial performance next quarter.

Four other factors give these statistical models greater accuracy.

  • The sensitivity of the market to analyst impact. A market with a small number of multi-million clients will feel more analyst impact than a similarly-sized firm with millions of tiny clients and smaller deals.
  • The sensitivity of the vendor to analyst impact. Firms themselves will have larger or smaller average deal sizes. However, some firms are in different parts of the market. Incumbent firms in mature segments might site in calm glory. Others might be selling to clients with a different risk profile, or with more extensive buying centres, and this might feel the impact of analysts more directly.
  • The firm’s analyst beta. The beta is the volatility of analysts’ mentions of the vendor, relative to the average in its market.
  • How far the firm meets expectations. Equity analysts keep an eye on whether managers deliver on the earnings expectations they set in the market. No one likes surprises: ostensibly-good surprises, like higher revenue, can flag increasing volatility and thus reduce enterprise value. From a statistical point of view, volatility is also excess unexplained variance from what our models anticipate.

This top-down approach is recognisably a brand equity approach. It poses the question: how much extra is this firm worth? What’s the premium created by analysts, or by analyst relations? The strength is clear: it shows the correlation between these factors, calculates which percentage of that is statistically explained by analyst behaviour, and values that as a percentage of the firm’s market capitalization.

The challenge, of course, is to narrow the gap between the top-down and bottom-up numbers. Top-down, we can see that a substantial market premium is directly correlated with changes in analyst advocacy. The bottom-up approach shows one of the causes: changes in the sales pipeline. The gap inbetween can be better explained by deeper analysis into the other factors. Some data is available that explains that impact, like mentions by investment professionals, policy makers and journalists who are influenced by analysts.

Our statistical approach can only go so far. It tells us, with a high degree of confidence, what premium in enterprise value is correlated with changes in mentions by analysts. The correlation is strong enough to predict future performance. However, only tells us what the premium is. Further research is needed to show which factors underly that premium. If your organization would like to join in the heavy lifting needed for this micro-analysis, contact me.

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