Why I’m Advising Bluecore

I first read The One to One Future by Don Peppers and Martha Rogers in 1997, four years after it was published.  As a marketer, the book made a big impression on me.  It was revolutionary stuff:  we should make the paradigm shift from mass marketing to individualized marketing.

When the book was published in 1993, newspaper ads were $75B/year, TV around $60B, the web browser was a mere three years old, and there were 623 total sites on the web.  There was effectively no web advertising market.  It was nine years before the Minority Report popularized a future vision of one-to-one advertising.  It was six years before Paco Underhill published Why We Buy revealing insights gleaned by manually tracking shoppers to understand in-store behavior [1].

Look at the subtitle: “Building Relationships One Customer at a Time.” You could use that in a webinar today.  The One to One Future was not just ahead of its time; it was so far ahead of its time that it could have equally been categorized under either “marketing” or “science fiction.”

Why?

  • It turns out, as with science fiction, that it’s easier to envision something than to build it. Remember, “they promised us flying cars and we got 140 characters.” [2]
  • Building individualized marketing systems required layers and layers of underpinnings that were simply not in place. You can’t do good personalization without a clean, real-time, 360-degree view of your customer.  Clean means a big effort into data quality and data profiling and typically either master data management or a customer data platform [3].  Real-time means real-time data integration [4].  360-degrees means pulling relevant data from virtually all of your systems.  Self-driving cars don’t work on cow paths.  Building those layers of requisite infrastructure has taken decades.
  • Marketing’s focus on the perfect offer was flawed. Say I found an offer with an 90% chance that you’d respond affirmatively.  Perfect, right?  But it was for a product that was out of stock.  The perfect offer has to be for the right product, in the customer’s preferred size or color, and available to sell.  We can’t just find the set called {great offers}.  We needed to intersect it with the set called {in stock and need to sell}.  This made a hard problem harder by pulling inventory and the supply chain into the equation.
  • Marketers got trapped in a vicious downward cycle of communications. Email click rates have nearly been cut in half over the past decade.  Marketing’s solution?  Send more emails to make up the difference.  Email vendors, who typically price by the email, were only too happy to accommodate.  That, however, is a short-term mentality.  More bad email with lower open and click rates isn’t the solution.  The same holds for ads and promotions.  Marketing needs to get out of this race to the bottom.  We need to focus on quality, not quantity.  And pay vendors for performance delivered, not communications sent, while we’re at it.
  • Finally, the retail industry needed to shift mentality from store-first to digital-first. Roots, as they say, run deep and retailers have long, deep roots in physical stores.  Bricks-and-mortar supposedly changed to clicks-and-mortar, but really, it was mortar-and-clicks the whole time.  The industry never really changed to digital-first from store-first.  Until Covid-19, that is.  While this meme, popularized in Forbes, was intended for many industries, it could have been custom made for retail [5].

So where does Bluecore fit in?

  • Bluecore is a multi-channel personalization platform. They’re building what marketers in the past dreamed of, but couldn’t build, because the infrastructure wasn’t there.  Now it can be built, and they’re building it.
  • Bluecore is an AI/ML company focused on retail analytics and personalization. I’ve blogged before that AI/ML is best applied to specific problems and not general ones, and this is a great example.  They are a closed-loop, retailed-focused application that gets smarter every day and with each new customer.  If you believed in the increasing returns of marketing leadership in technology markets before AI/ML [6], you should believe in them twice as much after.
  • Bluecore’s personalization understands both customer and product – and intersects them. Across a catalog of more than 250M products and SKUs, Bluecore can match customers and products at a 1-1 level.  It automates what would have been the work of a team of in-house data scientists.
  • Bluecore is paid for performance, not volume. They back up their performance claims with a pricing model based not on volume but on success.  This is a great example of superior technology enabling disruptive business model innovation.

Why am I advising Bluecore?  Three reasons:

  • As a true, blue marketer this stuff genuinely interests me. I love working with marketing companies on marketing problems.
  • It’s always about the team. I’ve loved working with Fayez Mohamood (founder/CEO) and Sherene Hilal (SVP of Marketing).  As a bonus, former Salesforce teammate Scott Beechuk is an investor and on the board.  I like working with people who like working with me and appreciate my inimitable (I inadvertently almost typed inimical) style when it comes to feedback.
  • The momentum and market opportunity. Bluecore’s a highly successful company, having raised over $100M in VC with top-tier investors, and they are pursuing transformational change in a $4T market.  The last 100 years in retail were all about stores, the next 100 will be about retailers meeting customers wherever they are.  And that’s what Bluecore does.

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Notes
[1] And why, to this day, you can find still baskets strewn throughout many retail shops as opposed to only at the entrance.  His work was kind of a manual predecessor to systems like RetailNext, whose founder I got to know through mutual investments in a prior life from StarVest.

[2]  Peter Thiel at Yale.

[3] Which weren’t to be invented for about 20 years

[4] The data warehouse was invented in 1992, with the publication of Bill Inmon’s Building the Data Warehouse.   Ralph Kimball would invent the star schema 4 years after that.

[5] Apologies to frequent readers for using this meme again – but I just love it!

[6] Tech buyers, and particularly IT buyers, tend to face high opportunity costs and high switching costs and are ergo generally risk averse.  This drives increasing returns for early market leaders.  Think:  no one ever got fired for buying IBM.

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