B2B Marketers Adopting AI To Streamline And Scale Personalization, Messaging And Consistent Customer Experience
As published by DemandGen,
Artificial Intelligence (AI) and machine learning are being baked or integrated into every aspect of B2B technology. Early adopters are seeing success leveraging AI to enhance and scale personalization efforts, offer content recommendations via owned channels and streamline the sales cycle.
Research from Demand Gen Report and Demandbase supports this claim, showing that nearly 80% of B2B marketing and sales professionals feel a significant amount (more than 20%) of their current marketing and sales applications will be AI-powered by 2020. Additionally, 70% feel AI-powered applications will help improve and accelerate the buyer’s journey by recommending next best actions.
“In addition to having AI help with targeting, AI is helping drive better performance when engaging target accounts in areas like ad buying [analyzing and optimizing layout, creative, copy etc.], content customization and web personalization [industry/account/role-specific content, next logical asset, etc.],” said Matt Senatore, Service Director for Account-Based Marketing at SiriusDecisions, in an interview with Demand Gen Report. “It also helps identify which accounts might be at risk of defection.”
Experts noted that the marketing department, in particular, is where AI has the biggest potential to impact B2B businesses. The study noted the following roles have the greatest potential to benefit from AI-powered applications:
- Demand generation (61%);
- Digital marketing (56%);
- Marketing Ops (45%); and
- Customer experience (40%).
“With AI, you can get specific on what the account wants in their content,” said James Regan, CMO and Co-founder of MRP, in an interview with Demand Gen Report. “We think the next place is AI-enabled content providers. That’ll be the next area where organizations such as MRP will bake in AI because that problem is real. You can only create so many content matrices; there will always be something needed — it’s beyond what a normal size company or agency can cost-effectively create.”
Early success from AI adopters show that B2B organizations will begin to adopt this technology rapidly to keep up with buyer expectations and their competitors.
Predictive Capabilities Evolve Into AI Algorithms
Experts noted that AI is already having a significant impact on marketing and sales technology (and marketers/sales leaders) today — except some might not associate AI as the driving force behind this. A notable buzzword in the B2B marketplace over the past five years was predictive analytics, which involves leveraging technology to predict prospects and accounts with the highest propensity to buy. This is considered a form of AI based on past purchase and engagement data from closed business.
Closing Data Loops To Connect The Dots With AI
An ongoing challenge discussed among B2B practitioners regarding AI adoption is the hesitancy to rely on decisions made by algorithms fueled by inconsistent, inaccurate or old data. Research from the Demandbase and Demand Gen Report study showed that close to half (46%) of respondents said that they are holding off on adopting AI because they “had a lack of trust in decisions being made without human oversight.” In addition, 42% said they “feared a general lack of control” in the decision-making processes of AI.
“AI isn’t calling the shots yet,” said Allison Snow, Senior Analyst for B2B Marketing at Forrester Research, in an interview with Demand Gen Report. “There has to be a lot of human oversight. You want to do a logic check to review the data and review the output. It’s worth digging deeper to better understand what data it deemed most relevant.”
Snow suggested that, to properly fuel an AI algorithm with the insight it needs, B2B marketers should:
- Review the data that will likely train this model; and
- Ensure the model has access to that data.
“If we continue with an example of using an AI-powered [algorithm] to predict churn, a B2B marketer should know how accurate the data records are that indicate churn,” said Snow. “Has the organization been disciplined in documenting the reason for churn, as reported by customers? Is there a drop-down list in a structured field, in which a salesperson or customer service rep is expected to provide a reason for non-renewal? If so, have they been using that field consistently? Or have they carelessly just selected “non-renewal” from a drop-down and moved on?”
Snow added that the data points marketers plan to use are worth an “audit” of some kind. Then, the algorithm must have access to that insight. “Again, is it in a structured field or non-structured? If it is non-structured, does the tool have the specific AI tech to interpret the data, like text analytics that can learn from unstructured data?”
Ultimately, B2B marketers need to know the formula for model-making with machine learning is:
Data + Algorithm = AI model
“Algorithms are inherently objective,” Snow concluded. “Therefore, machine learning models are only as good as the data you use to train them.”
MRP provides predictive customer acquisition software and services. For 15 years, clients have relied on MRP to help them achieve their revenue goals by combining cutting-edge predictive analytics with a full suite of account-based marketing services to acquire new customers, faster. MRP has 10 offices, 550 employees and covers over 100 countries around the globe.
Visit www.mrpfd.com for more information.