In the last few weeks I attended two SiriusDecisions events: SiriusDecisions Tech Exchange Summit in Austin and a half-day session on predictive analytics in Boston. These events featured analysts and demand generation/ABM marketers sharing their research, insights and case studies. Topics and presentations focused heavily on martech for B2B demand generation, but also emphasized the importance of a strategic marketing vision, clear and defined processes, and the roles and skills needed for successful implementation, adoption and execution.
As a digital marketer, here are the four themes and emerging trends that stood out to me:
Speaking with event attendees, I found that most have either made investments in ABM or are in the process of doing so. Many of the sessions included case studies featuring ABM challenges and solutions, and ABM vendors were well represented at the Tech Exchange Summit.
In the sessions, we learned that there’s often a temptation to kick off ABM initiatives by having sales and marketing teams identify target accounts and then come up with plans and campaigns to engage those accounts. The problem with this tactical approach is that after the completion of a series of campaigns, ABM becomes just another initiative that falls by the wayside and is replaced by the next “new” thing.
To adopt ABM as a long-term strategy requires executive sponsorship, training, the right skillsets, a few key integrations in your martech stack, and more. ABM is a mindset, not just a list of accounts to target. It’s an approach that’s still maturing and in need of clarity. For example, what’s the role of the content marketer and the digital marketer within an ABM framework? How do KPIs and success metrics change? In recent articles we’ve weighed in on content marketing and ABM as well as how digital marketing fits in.
Predictive Analytics and Machine Learning
According to SiriusDecisions analyst Kerry Cunningham, predictive analytics solutions for demand generation are still in their infancy, but the segment is growing rapidly and B2B marketers are eager to adopt and integrate these technologies with the rest of the stack. The first solutions started to appear about five years ago and can help you predict which companies are high-quality prospects, which ones show intent to buy an offering from you or your competitor, which of your leads are most likely to buy, and which of your customers show signs of non-renewal. Attendees were excited to hear about advances in this segment and to learn from case studies. They were also interested in guidelines for evaluating vendors. My key takeaways include:
- Demand generation challenges come in different forms. Some companies struggle with generating enough leads while others are challenged with prioritizing an enormous number of leads. Some have identified conversion challenges further down the pipeline as an issue. When evaluating vendors, make sure that the offering and underlying algorithm is designed to solve the main demand generation problem that your company is experiencing.
- Like personalization, predictive analytics has machine learning at its core to uncover patterns, trends and outliers from internal and/or external data. Since most of us aren’t data scientists, how can we compare each vendor’s algorithms and modeling processes to determine which is the right solution for our situation? You may not be a rocket scientist, but you do know your business, your challenges and your team. When speaking to vendors, if they can’t clearly explain how their algorithmic models work and what they do, cross them off your list.
- To be effective, these solutions rely on substantial amounts of internal and/or external data. Make sure you have the right level of integration across your stack to get to as much of the right data as possible. Also, talk to your vendor about services like data enrichment and access to third-party data to supplement the sources you already have.
If alignment had been the keyword in a drinking game at these sessions, we wouldn’t have made it past 9:30 am. The alignment discussions took on a few different flavors:
- Between sales and marketing. While not a new discussion, it takes on a different angle with demand generation marketers. Today’s martech stack and corresponding campaigns plummet in effectiveness if sales and marketing aren’t in sync on definitions, processes, responsibilities, campaigns and target accounts.
- Between stages of the customer journey and all the channels used by prospects (web, search, mobile, email, social, human). The goal is to have a continuing, relevant conversation with target accounts and the individuals who comprise those accounts. If your email messages to a prospect assume she is in the evaluation stage, but her experience on your website is in the context of someone who is at the awareness stage, you’re not helping her progress forward in the journey. Ultimately, this means that you need to break down the silos between the web, content, marketing operations and sales enablement teams who are responsible for communications at different stages. But you also need to be aware of what each team is doing to build momentum together instead of creating disjointed campaigns.
- Between execs (who set goals and make decisions on marketing technology) and day-to-day end users of marketing technology. To ensure alignment, both groups need to define and agree on goals, metrics and roles. When evaluating marketing technologies, those with buying authority need to include end users early on in buying decisions.
Underscoring and ever-present in these discussions was personalization. In fact, personalization came up as part of each of the three previously mentioned themes:
Personalization and ABM
Now that your sales and marketing efforts are focused on key accounts and segments, make the conversation relevant with targeted customer experiences and messages to increase engagement and shorten the sales cycle. A robust personalization platform lets you understand which individuals belong to which accounts and enables you to engage with them in the right context, in real time.
Personalization and Predictive Analytics
These two technologies complement each other in a number of different ways. Here are just a few:
- If you’re using predictive analytics to identify new leads, don’t deliver a generic web experience when one of these leads visits your site. Leverage your account knowledge about each of these new leads to start a relevant conversation that leads to quicker conversion.
- For prospect prioritization, use a feature like Evergage Guardian to monitor changes in intent from your target accounts and pass along that information to account managers for immediate action.
- For retention and upselling, personalization can identify the right time to deliver upsell or cross-sell messages, or to intervene to reduce churn depending on user behavior and adoption.
Personalization and Alignment
Personalization solutions can track intent at both an individual and account level. This data can be used to provide relevant web, mobile, retarget ads and email experiences, but it can also be shared with sales teams so they can have more productive sales conversations with prospects. This ensures better alignment between sales and marketing and provides consistent messaging at each stage of the journey.
Rapid growth and innovation in martech stack trends and offerings are driving excitement but also some confusion. Although it’s tempting to quickly adopt these solutions, a better approach is to think more strategically and evaluate them in the context of how they can integrate and build on each other to solve current and emerging demand generation challenges.
To learn more about how Evergage can help you address some of these challenges, request a demo today.