My Next Chapter: AI & Market Research

Yesterday, I posted about my last day at Bloomreach. Here are my plans for the next chapter!

It's a bit much 😂...the TL;DR is that I’m taking the next few months to work on a few startup ideas around AI for market research :). 

A Realization

I came to a realization this summer that Bloomreach needed a “full stack” product marketing leader, someone that loves outbound product marketing (messaging, launches) as much as they love inbound marketing (research, GTM planning). I do enjoy all the PMM things, but mostly I love research and getting data on the voice of the customer. That was to a fault sometimes at Bloomreach because I would get deep with a research project when I probably should have put more time into a launch. 

Now I’d like to turn that research and data tendency into a strength.

Space to Create

Raj said once on a podcast that it’s impossible to nurture an idea while having a full-time job. I don’t have the groundbreaking idea yet, but I have some ideas about market research and about using the newfound AI capabilities to make it better, so over the next few months, I’ll be in the space of exploring and researching.

I recently finished a book called “The Man Who Knew Infinity” about the brilliant Indian mathematician Ramanujan. He failed college twice, then couldn’t get a job after failing as a tutor. However, the setbacks gave him “freedom to think, to dream, to create” and it led to some of his greatest discoveries. 

I don’t claim to be a genius creator, but I’m giving myself some space to create. Over the next few months, I’ll be spending time exploring three areas 🧭🏔🔍, so please reach out if you have ideas or thoughts or people you might know who are working in these areas.

THREE AREAS TO EXPLORE + ONE BONUS AREA 

#1 Ideal Customer Profile (ICP)

Anyone involved in go-to-market planning is trying to first answer the question, “who should we target?”

Last year, when our marketing and product teams created our ideal customer profile and target markets, it was an extremely extensive process with tons of data mixed with a lot of intuition. Christine Reyes had a great framework from Forrester called “Relative Targeting”, but trying to collect data about past, present and future trends and select the right markets was really challenging. 

I’ve spoken to others about how they approach ICP - here’s a sampling of what most teams are faced with analyzing:

Past: 

• Trends of firmographic data among past wins (Zoominfo, D&B or other Revenue, employee, geo data)
• Trends of technographic data among past wins (HG Insights, Builtwith, etc.);
• Win rates and renewal rates from CRM

Present:

• Companies in market for our solution (6sense)
• Existence of competitors, watering holes, events, etc.
• TAM / size of market (including economic forecasts)
• Internal readiness
• Product usage data from Pendo or Mixpanel on product usage

Future: 

• Analyzing company intent (e.g. from earnings reports, vision statements, hiring plans, etc.) (Rule5.io is one I was introduced to recently)
• Market predictions and economic forecasts (e.g. eMarketer)
• Expected budgets (HG Insights)

I spoke to Lance Johnson at OpenGTM which is a startup with a quick “persona engine” to take all this data and help you build an ICP. 

Could the right company unify all these datasets and build an ICP tech that could feed into your ABM system or sales ops and help marketing and sales teams better prioritize and focus? 

What if your marketing events team could ask an AI to analyze an upcoming tradeshow and give it an “ICP fit” score after reading past presentations and attendee lists? 

#2: Market & Customer Insights (point solutions)

Once you have an idea of who to target, how do know what those markets care about? How do you understand the heartbeat of those customers and market in order to build the right things and send them relevant messages?

There is no shortage of market information, but Product Managers and Product Marketers don’t have nearly enough time to consume it all. Take a PM or PMM at Bloomreach, for example, trying to understand the retail market. Can they read and consume all the research from Shop.org, eTail, Digital Commerce 360, McKinsey, various analyst retail research, eMarketer studies, etc. in order to make product roadmap or GTM planning decisions? 

The problem is that information about the market lives in dozens of places coming from many vendors: 

• Customer Calls, Sales Calls & Emails (Pattern.ai, Tribyl)
• Call Center interactions (Observe.ai)
• Review sites: (G2, Capterra)
• RFPs (Loopio, etc.)
• Market research (e.g. for retail. This varies by industry of course): Trade publications (Digital Commerce 360, Shop.org,  eMarketer Analyst research
• Surveys (Qualtrics, SurveyMonkey)
• User analytics (Pendo, Mixpanel, etc.) 
• Internal win/loss programs
• Interviews with experts (GLG, AlphaSights, Guidepoint, etc.)

In addition to the sheer number, most of these sources require specialized knowledge to get insights from them (just ask Sanjeev Somani or Greg Tapper how difficult it is to understand a call transcript). I spent the last 12+ months working with Tribyl and Pattern.ai trying to get insights from Gong calls. That’s a feat in itself and we are just getting started.

It feels like there are a dozen companies to be built that can specialize in extracting insights and summarizing them from each of these sources. I hope that many providers (e.g. eMarketer, analyst firms) could provide some of that to their customers, but it’s still going to be overwhelming amount of info if it’s coming from each vendor. 

#3: Unified Market Research (aggregators of insights):

That leads me to the last area of research for me, which is the vendors trying to pull it all together and be a source of truth.

This topic has been on my mind since 2021 when Arjé Cahn and I tried to get everyone in the PM and PMM teams to log every customer interaction into Airtable and get insights from it. I wrote it about this last year, but the short summary is that it was a high effort, low return project. Raj asked us to stop working on it eventually. Fortunately, we went back to the drawing board and turned it into an extensive win/loss program that was very impactful, but I have always thought about going back to the original vision. 

The exciting part is that AI will make this much more scalable. Productboard has been adding AI to become the source of truth and pull it all together. New companies like Zeda.io are using AI to help with the “product discovery” process by pulling in support tickets and product analytics. 

There have been many companies that have attempted this (GetScopeAi, etc.) and some new start-ups in the space (Artifact.io), but it seems AI can finally help make this possible.

I think an opportunity could be that while many companies are working on pulling in information from Zendesk, Mixpanel, etc. (sources of product feedback), there is an opportunity to bring in more market knowledge and market research into a central place. Marketers and salespeople need these insights as much as product teams do. 

Note: credit to Greg Tapper here. He was the one who shared with me his simple way of describing go-to-market strategy/planning as being simply “who to target, what they care about, and how to reach them”. 

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#4: AI for Good (more directly)

Lastly, I have to admit that while I’m excited about AI for market research and ICP, I get jealous when I see AI being used for more directly impactful social uses. Khan Academy and the other MOOCs were a big step forward in democratizing education - AI will help democratize it even further. AI is helping to better diagnose brain tumors on the operating table. The economist last month talked about how it will change science itself. And I can’t help but watch the wars and the earthquakes and the income inequality around the world and not crave wanting to help in some more direct way using AI. 

In college, I chose to be an economics major after reading about how development economists were using data to figure out which programs were most effective in help to get more girls in school or what caused income inequality. “Development Economics” and “Economics for Education” were my favorite courses and I did a short internship with the BYU Center for Social Enterpreneurship (now the Ballard Center for Social Impact). 

When I’m not at work, this is what occupies my mind. I read both of Matthew Desmond’s books, “Evicted: Poverty and Profit in the American City” and his more recent “Poverty by America”. I read Melinda Gates “The Moment of Lift”. I volunteered for awhile with Datakind back in 2017-2018 which is a great organization that enlists data scientists as volunteers on social impact projects. 

Aside from that, I haven’t really found time to get involved or pursue opportunities with non-profits or social entrepreneurship. When LaDon Linde left Bloomreach, he went to work for my church in the “Perpetual Education Fund” which provided education loans for people in many different countries. I remember being impressed that he could leave a prestigious tech job and go work for a non-profit. I hope someday to have the guts to do that.

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Anyway, lots of work to do, but I’m excited for whatever the next chapter brings. I’ll look forward to sharing more as I go!