Prediction #1: The year of anti-ABM?
If you follow any of the top technology marketing influencers, you’ve probably heard (too many times I’m sure) that 2016 was the year of Account-Based-Marketing (ABM). It’s hard to argue they were wrong.
It seemed like everyone I spoke with this year started moving their entire marketing organization (and marketing software stack) away from traditional one-to-one lead marketing. Instead, they invested in a new account targeting approach with the hope of faster growth through focus. It was a big bet. They put all of their future growth (and pipeline) on a very specific list of companies and similar “look-alike” accounts. But did it work?
While I believe the general concept of ABM is a good one, 2016 was a learning experience for the organizations that went “all-in” on this approach. We saw companies invest heavily with new ABM-focused MarTech vendors, and move quickly to reorganize sales and marketing departments to execute on this new strategy. For some, this rush to ABM finish line led to a lack of strategy and not nearly enough time spent defining their target account lists.
In 2017 I predict there will be an unfair backlash against ABM. Companies who invested in ABM and failed will believe it doesn’t work. Some who initially considered a move to ABM, will not do it at all. I don’t recommend giving up, though. If you have the systems in place and the business units organized to support ABM, you’re already half way there. If ABM didn’t deliver as promised, your first step should not be to rip it all up. Instead, take a hard look at your targeted account list. How did you create it? Did data drive this decision, or was it only based on guess work or sales feedback? As we will all learn, when it comes to choosing ABM target lists, context matters.
Prediction #2: Context Matters
Relying on demographic filtering is where many ABM lists go wrong. I’d even argue that thinking too much about demographics at all is where lead generation goes wrong. In 2017, let’s all consider valuing intent and activity over demographics.
I’ve written about this before, but I’m still surprised that we’re all still so focused on demographic targeting. I fully understand that some technology has certain demographic requirements. A healthcare software solution only works for hospitals and some technologies require a certain number of seats, but for a significant portion of the technologies we all sell; demographic targeting is not the best way choose target accounts or generate “quality” leads.
I shouldn’t be surprised we still think this way. Up until now, this is how it’s always been done. You bought radio, TV and print ads all based on audience profiles, which was just basic demographic targeting. If you wanted to target someone most likely to buy a Mercedes you would advertise in Rich Person Magazine, where everyone subscribed is aged 30-60, employed full time, makes over $300,000 a year and owns at least 2 cars.
Today things have changed. Instead of the primary demographic filters like Rich Person Magazine offers, you can target the Mercedes buyer through data-driven options. You could purchase data from car research sites of people who’ve specifically requested information on Mercedes models in the last 30 days. How about receiving contacts of people who downloaded independent Mercedes review guides? What about being able to send a mass text message out to everyone who was checked-in at a Mercedes dealer based on the location of their mobile phone? This buyer intent data is far more valuable than a potential customer’s age or marital status. Context matters.
I predict 2017 will be a growth year for the intent data business, driven by a need to improve customer conversations and conversion rates. The new problem tech marketers will face is not being able to access and purchase intent data, but finding ways to decode it and use it for outbound marketing campaigns. Demands for better data source integration tools will spawn an entirely new MarTech service industry.
Prediction #3: Hello marketing IaaS (Integration as a Service) vendors
If you’ve moved past the old days of demographic targeting and started leveraging multi-sourced intent data, you’re probably struggling with ways to compile it, understand it and execute targeting marketing campaigns using it. Don’t worry, you’re not alone.
My prediction is we will see a new crop of companies who will help us deal with this. Some will be familiar faces with new solutions, others will be new startups. The problem right now is not gaining access to intent data, we have a lot of sources for that. It’s compiling all these sources, understanding what data is valuable, ranking them based on intelligent rules and then allowing marketing teams to execute very targeted campaigns based on it.
Here are a few of the common data integration problems I hope to see addressed in 2017:
– Contact data: We all see conflicting contact data from multiple sources. Help us understand what contact details are correct and make sure the contact data in our database is always 100% accurate.
– Intent data: How should we score intent data for the most accurate picture of a future technology investment? For example, is a white paper download always the same score, or is it more valuable when it’s downloaded on our sites vs. a third party site? I want a solution that allows me to input all the intent data I have (across all first and third party sources) and then rank prospects and accounts for me based on this.
– The real buyer journey: Input all the first and third party data I have and show me everything this account has done (with me and without me). Use this data to give me an accurate picture of where the account is within the buying cycle. Based on this analysis, recommend the next steps to take. I’d also like to feed in current customers and look back at their journey to recognize the most common paths to sale.
– Intent-based marketing and sales workflow: I’ve seen a few new companies headed in the right direction with this. Integrate all the data I have and provide next steps and alerts for the marketing execution and sales teams. Tell me how and when I should follow-up with a prospect based on the actions they’ve taken.
– AI to help me make better decisions: Leverage artificial intelligence and multi-sourced intent data to take predictive actions for me. This will be the first significant step towards our future… machines as marketers.
Prediction #4: The rise of the machines
We’ve already seen IBM Watson embedded into technologies to provide insight into buying signals and improve customer experiences, but this is just the start. As more MarTech vendors develop their own artificial intelligence systems, AI will be everywhere.
Don’t be surprised when you see more sophisticated tools hit the market next year that allow you to use natural language queries to understand customer base, but also execute on your marketing campaigns. I see a future of marketing automation tools that will create individualized campaigns, decide on the message, write the copy, send the mailing and then set up endless streams based on individual actions. All we’ll have to do is literally tell it when to start.
We obviously won’t reach this level by 2017, but I do predict that AI will continue to play more of a role in our everyday lives. In the end, it will be computers, not humans that will create high-value, long-term, happy customers. Ironically, computers will understand customer needs better than we ever could.
Prediction #5: Happier customers?
If we do the right things, 2017 will be the year of the happy customer. Happier customers stay with us longer, invest in additional services and most importantly tell their colleagues how great we are (in person and on social media).
Doing the right thing by our customers may mean doing things differently, though. There is a reason our prospects are not picking up the phone as much anymore, blocking our ads and filtering our emails into SPAM folders. We took advantage of them and now we’re paying the price.
So, let’s use the data we have and the tools we’ve invested in to treat our customers with the respect they deserve. Email them with content they want, call them when they’re ready, and make it easy to engage with us (online and offline).
With that said, here are a few final thoughts on how we can all make our customers happier in 2017:
– Leverage intent data to market to the right person, with the right content, at the right time.
– Don’t “over-market” to the wrong people. There is too much noise to win by sheer force. Contacting non-relevant people with non-relevant content will backfire.
– Make it easy to learn about your solutions and contact customer service. Eliminate the content gates if you can, and use new technology to make communication (phone, email, chat) easier.
– Finally, remember your “contacts” are human. They have families and interests and lives outside of buying your products. Speak to them that way. Cut the buzzwords and the hype and focus on how you can help them. It’s amazing what a difference it can make.