By Doug Bewsher, Leadspace CEO
Over the past few years, billions of dollars have poured into marketing automation tools. While these solutions, coupled with cloud computing and Big Data, have revolutionized the way startups find, attract, and retain customers, the real opportunity lies in predictive apps. These tools yield unprecedented new capabilities to fine-tune audience targeting, work more efficiently and close more deals faster. For young startups that survive based on their ability to quickly attract customers, predictive technology is a huge asset.
Data has already transformed the way startups learn about their customers and make decisions. Before, collecting and processing information about customers was time-consuming and expensive, and thus difficult for smaller companies. Now, cloud-based analytics tools give startups access to incredible amounts of information about their customers, and marketing automation tools help them channel that information into real results. Targeting the right customer with the right offer at the right time and identifying quality sales leads no longer requires hours-and-hours of research or guesswork. However, most analytics tools are still reactive. They uncover insights about things that have already happened to drive future decisions.
Predictive marketing and sales technology takes businesses from reactive to proactive. These apps learn who your ideal customer really is and their likely behavior. Using this information, they detect those customers’ intent in the moment, trigger an appropriate action in response, and optimize the response for the device or channel. For example, these apps can anticipate who the most promising sales leads are likely to be, or which existing customers are in danger of leaving. Based on this data, startups can step in with a desirable offer or loyalty program at just the right time. Companies with limited resources are able to focus the resources they do have where it really matters.
Nearly 80 percent of companies have adopted marketing automation technology or are planning to this year. The market is worth billions of dollars, yielding a number of high-profile, high-value acquisitions, such as Salesforce’s purchase of RelateIQ and ExactTarget, and Oracle’s buyout of several marketing technologies. The money is now moving towards predictive apps. Funding for this emerging sector skyrocketed between 2013 and 2014, fueled by investments from the industry’s elite venture capital firms.
These trends indicate that in a few years, predictive apps will be a staple of the standard marketing stack that startups use to manage their marketing and sales. New members to this stack will include customer success tools like Totango, InsideSales for sales data, and demand-generation solutions, such as Leadspace. Furthermore, integrating predictive solutions will drive even greater impact on business revenue and growth from existing automation platforms, like Salesforce and Marketo. Finally, startups will have the actionable insights they need about their customers in advance, which in turn will lead to more conversions and higher returns.
However, we are not there yet. Eighty percent of B2B marketers rate ‘generating quality leads’ as the technique with the highest profit potential, but this is still a challenge for many startups. The torrent of available data can actually be counterproductive. It is so overwhelming and incomprehensible that marketers default to manual methods and intuition, despite having access to more sophisticated tools. Predictive apps solve that problem by doing the legwork for you. They analyze data from multiple sources to identify patterns, and use machine learning to predict which leads are the most likely to convert. Marketers gain a much clearer picture of their ideal customer and how to reach them. Startups don’t have to worry about paying for a team of data scientists to back their marketers up.
Predictive applications may seem too good to be true, but it is important to understand that they are only as valuable as the data you feed them. Startups that want to be data-driven may find they don’t have enough data to truly benefit, especially if they are less than two years old. They should draw on both internal and external sources, depending on what they are trying to achieve. In certain cases, such as retention modeling, internal datasets are what drive the most value. In other cases, such as demand generation and new business acquisition, data from external sources like social media and the web is useful because startups don’t have much information about the prospects to begin with.
Ultimately, all marketing and sales is about people. It is still individuals that make decisions, not organizations. Predictive tools that focus on which companies make good prospects only reveal part of the picture, because reaching out to the wrong person within that organization can lead to failure. Sales teams can still waste time calling leads who don’t have the authority to purchase, or initiate conversations without the right contextual information, and startups just don’t have that time or money to waste. That’s why effective marketers must make it a priority to understand individuals, not just companies, and learn as much about the customer as possible.
As new companies, startups have a blank slate to build a data-driven organization from the ground-up. I envision a future where companies of all sizes use demand generation platforms that are powered by data, leverage predictive analytics, and are well integrated with their other CRM systems. Web and social data will become the best ‘database’ for finding prospects. Sales funnels will be filled with only high-quality leads that are likely to convert, and predictive apps will help create context around those leads so salespeople know exactly how to begin the conversation and when to follow-up.
Marketing automation quickly went from a competitive edge to a competitive necessity, and I believe the same will soon be true of predictive apps. They will level the playing field for startups that have significantly less resources than dominant incumbents because what will matter is your data, not your money. Sales and marketing will never be the same.
