Inside the black art of analyst targeting & tiering
In my last post, I concluded that analyst targeting isn’t evil, just necessary. So why continue with the necromancy theme?
Because targeting & tiering is a black art – it’s part knowledge, part experience, part intuition. It can be taught, but it’s certainly not a science. Targeting is a process, and like any process, it has a series of steps.
This is the easy part. There are thousands of analysts worldwide, but you want to exclude those that have no relevance to you. The simplest way is to develop a short list of keywords which are relevant to your business. Say you sell CRM software that includes some business analytics capabilities, then you might choose “CRM”, “salesforce automation” and “business intelligence” as your keywords. If you have tailored solutions for specific industries, you might add “government” and “supply chain” into the mix. If you have a cloud-based delivery model, you might want to add “SaaS”, for example.
But don’t get carried away with too many because you’ll spread your net too wide. If a solution accounts for only a small percentage of your business and isn’t a growth or focus area, ignore it. Be careful not to get too specific with your keywords – unless you’re looking for deep insight in a narrow field – or you run the risk of screening out everyone.
AR program objectives
This is the definition part. What are you trying to achieve with your AR program? Are you trying to build market awareness, what my colleagues at KCG call exposure? Or are you trying to reach analysts who have direct influence over technology purchasing decisions? Do you want to engage with market or technical experts who can help you with your competitive positioning? Are you looking for firms who’ll produce sales collateral for you? Whichever way you plan to go, you need to understand your goals and align accordingly.
Research, research, research
This is the time-consuming part. Whether you’ve got access to a commercial database such as ARInsights’ ARchitect or an internal analyst list, you’re going to need to add to your knowledge before tiering your analysts. Use your keywords to narrow your search. Most analyst firms publish analyst bios on their websites, and most allow you to search at least the titles of research they’ve written. The quality of this information – and its usefulness – varies considerably, but it’s stuff you need to know. Look more broadly – at LinkedIn profiles, social media usage, media articles they’re quoted in, conferences they’ve presented at and whatever else you can turn up. Learn from it, but don’t take it as gospel.
This is the interesting part. As part of the process of defining your AR program objectives, you made some decisions – consciously or otherwise – about the type/s of analysts you want to engage with. This means that certain analyst behaviours and characteristics are more important to you than others, and you need to weight them accordingly. If you care about direct user influence, then analysts with large end-user enquiry loads will be more important; if you want to drive media exposure, you’re going to value media-hungry analysts higher; if you believe strongly in the power of social media, you’ll be looking more closely at the Twitter junkies.
Pick your criteria – usually five to six is enough – and evaluate your analysts accordingly. Rate them, rank them, throw darts at them – how you do it is up to you. Most AR pros use a spreadsheet or database to enable sorting & searching, but one of my clients has me mapping analysts onto a Boston Consulting Group chart (think Gartner’s Magic Quadrant framework) which is a remarkably powerful way of visualising analyst value, particularly when educating senior executives.
Some analysts will naturally float to the top, but most won’t.
Experience & intuition
This is the fun part. What you have so far is lots of information. What you don’t have is clear answers about which analysts are really the most important and influential.
Most of the information you have at this stage is flawed or incomplete. Boilerplate bios on analyst websites tell you what the analyst thought s/he covered when s/he started the job, not what s/he actually does now; LinkedIn profiles are bloated with self-serving achievements and vague references; published research titles give some insight into coverage, but not much about depth of domain knowledge.
There is no substitute for experience. And applying it to the information you’ve gathered is where the science turns to art.
Over the years, I’ve engaged with hundreds of analysts. Some have worked with me; a few were clients before they became analysts; many have attended briefings I’ve organised; others have participated in interviews for my AR measurement processes; with too many to count, I’ve shared meals, coffees & drinks in cafes, hotels, airport lounges, breakout rooms, and everywhere in between.
During those interactions, we’ve learned much about each other, and developed trust in many cases. It is the insights from those interactions that I’m able to apply to the tiering process – knowing the types of clients they really work with, what their workload looks like, the way they engage with senior executives, the types of questions they ask, the depth of their domain knowledge and so much more.
Quite simply, I’m more comfortable making a judgment call about an analyst I know well than one I don’t. But what if I don’t know the analyst? Well, experience & intuition still have a role.
You need to be able to read between the lines. Simple things like analyst titles, reporting lines, co-authors, media profiles all tell a story, but you need to know what you’re looking at. So all that information that I said was flawed or incomplete still has a value, if only you know how to interpret all the different pieces.
To target & tier effectively, you need to make judgment calls, and that only comes with experience. You can’t teach it, but you can learn it.
But what do you think?