Creating Smart Audiences for Investment Management
Audience segmentation is probably one of the most important steps in B2B marketing, but for many, it is also one of the most difficult, particularly in markets where there are a lot of prospects showing complex and fragmented buyer behavior. Not only does choosing the wrong audiences to target lead to a waste of marketing spend, but it can also cause a lot of frustration across the rest of the business.
In the investment management industry, it’s not unusual to hear many marketing teams define their target audiences in broad terms – for example, targeting financial advisors across Europe. Beyond these broad definitions, there can be a gap in understanding how the nature and behavior of an audience can change, leading to a one-size-fits-all strategy from a segmentation perspective. It can sometimes feel that for many marketing teams, audiences are inherited over time or are based on subjective criteria without much analysis of how relevant they are to the current market. So, where do we start when it comes to redefining how audiences are segmented? There are many different methodologies today a company can choose from. Working with a number of investment management firms, we have found using a mix of the following methods to be particularly effective:
· Firmographic: Involves grouping customers based on the attributes of their business including size, location, and industry.
· Potential Value/Revenue: Groups customers based on how much revenue they are likely to generate. This is a particularly good method to use with account-based marketing (ABM) where companies are tiered into different groups based on the potential revenue they can produce.
· Intent/Behavioral: Customers are grouped based on their specific behaviors or buying intent, including how they engage with marketing content, what they are searching for online, the level of investment/AUM they have, etc.
· Buyer Journey: This method segments customers based on where they are in the buying cycle. It is particularly valuable for firms with long sales cycles and multiple stages where they can send personalized messaging that matches the needs of different stakeholders at each individual stage.
· Persona-Based: Groups customers based on their specific roles or functions. This works well with buyer journey segmentation where roles and functions require more focus at different stages of the buyer journey.
Once a firm has decided on the methods most relevant for their business, the next step is to gather all the required data using a combination of quantitative and qualitative sources including CRM systems, market research, industry reports/databases, surveys, interviews, and marketing analytics. This can involve a lot of work and effort for many firms. There’s not only the effort and resources required to gather all the necessary data, but there’s a need for expertise in storing, cleansing, and enriching it before it’s ready to be analyzed and used to create audiences. Analyzing the data can bring its own set of challenges, particularly in trying to manage multiple audiences that overlap with each other. Even when that’s possible, there’s the issue of how many audiences can a firm realistically support at any one time, particularly if each requires a high degree of personalization.
And, while lots of work and countless hours are necessary to create audiences, many firms make the mistake of treating the exercise as an ad hoc event that can be updated periodically. Audiences are constantly evolving rather than staying constant. There’s a high probability that a specific audience today will look totally different in a week or a month regardless of the underlying method used to create it. This is because the nature of the B2B buyer journey itself is constantly changing. On one hand, prospects are constantly moving through different stages of the buying process, starting off at an awareness stage and progressing through to an eventual purchase. There are also multiple decision-makers and influencers who have a different degree of impact at individual stages of the buyer journey. Added to that, the behavior and buying intent of prospects are in constant flux and becoming much more fragmented. The challenge for many investment management firms is how to manage this constant change while at the same time building meaningful audiences that reflect what’s actually happening in the market.
To overcome these challenges, we are seeing more investment firms starting to explore AI and predictive analytic solutions which offer the speed, intelligence, and scale to build and segment audiences that are smarter and more aligned with market and customer behavior. A big advantage of using AI is that it enables the creation of dynamic audiences that are constantly refined and updated as the underlying criteria change. Unlike static audiences, dynamic audiences offer the benefit of being able to respond in real time to changes in customer behavior and market conditions, ensuring marketing teams can quickly adjust their marketing strategies as required. Another big advantage lies in the scale AI can offer in segmenting audiences. Not only has it the ability to compile and integrate huge amounts of data from multiple sources and segmentation methods – firmographic, behavioral, intent, buyer journey, and persona-based – it can process that data in real time enabling the segmentation of different audiences at scale without the corresponding increase in manual effort or cost. It can also automate the personalization required for each segment, delivering it at the appropriate time based on where a prospect sits in the buying cycle. As AI moves to take up a lot of the heavy lifting and effort associated with audience segmentation, predictive analytics using historical data and trends will empower firms with the ability to forecast and anticipate customer and market behaviors, predicting when prospects are in an actual buying cycle and most likely to convert.
In conclusion, creating smart audiences in investment management is more than just adopting new technologies: it’s a change in mindset focused on leveraging intelligent real-time customer and market data. Using a combination of AI and predictive analytics, firms can make the successful transition from static-based audiences to dynamic ones that not only enable more effective marketing strategies but allow them to quickly scale their personalization efforts ensuring they reach the right audiences at the right time with the right message.
At PredictiveB2B, we work with investment management firms to help scale their audience segmentation efforts. We help firms to build audiences that dynamically update in real time taking into account changing customer and market behaviors. Our advanced analytic expertise enables firms to predict when audiences are showing the highest buying intent and are most likely to convert.
