DEMYSTIFYING B2B PURCHASE INTENT DATA: UNDERSTANDING THE BASICS

Transcription

1 DEMYSTIFYING B2B PURCHASE INTENT DATA: UNDERSTANDING THE BASICS

2 THIS REPORT COVERS THE FOLLOWING 1 What is intent data? 2 Does intent data work? 3 What are the benefits of intent data? 4 What are the different ways to capture intent data? 5 How does the intent data from the three methods differ? 6 What are common pitfalls with intent data? 7 What are the recommendations to move forward with intent data? 2

3 1. WHAT IS INTENT DATA? B2B purchase intent data (intent data) captures online buyer behavior, tracking a specific company s research level with regard to a specific product/solutions category. This data is used to generate an intent score. As research activity with regard to a given category increases, the intent score also increases, reflecting an ever higher likelihood that the researching company will purchase products or services in that category. 2. DOES INTENT DATA WORK? Aberdeen has worked with clients to conduct over a dozen assessments of intent data accuracy. To begin, these clients provide us two things a list of companies, and a particular product offering. The list of companies should include a mix of known opportunities, as well as companies that do not have an active opportunity. This mix helps identify false positives. 3

4 INTENT SCORE VS TIME Research Activity Opportunity Open Date Opportunity Close Date Account 5 INTENT SCORE Account 6 Account 7 TIME We then go back in time and look at historical intent data from multiple intent data providers in order to track the research activity of the companies in question. The visualization shows what you would expect: Active research activity increases above a baseline at the beginning of the process and continues to increase, with some plateauing, until the end. Inaccuracies, including false positives (identifying intent where there was none) and false negatives (missing intent signals for known opportunities), varied significantly by intent data vendor. An example of a visualization of the results can be seen above. The black line represents research activity over time. After we performed our analysis, the client added the teal dot (opportunity open date) and red dot (opportunity close date). Accuracy rates (the percentage of total accounts with known opportunities where the data revealed measurable increases in research activity) on the tests we ve conducted range from 53-91%, depending on the category and the intent data provider. In other words, intent data providers are not all the same. Companies need to vet intent data providers in order to select the one that can provide the most accurate data for their market. 4

5 3. WHAT ARE THE BENEFITS OF INTENT DATA? Most B2B companies are flying blind when it comes to identifying companies currently in-market to buy what they sell. Indeed, the majority of B2B marketing budgets are spent trying to make up for this lack of visibility with highly inefficient and non-scalable tactics. As our tests have shown, however, it is possible to determine, well ahead of a buying decision, which companies are in-market to buy, with up to a 91% accuracy. EFFICIENCY With this information, marketing and sales efforts can be focused on precisely these accounts, dramatically reducing the amount of marketing dollars and sales time wasted on companies that will not purchase. PIPELINE QUALITY Since the opportunities being created are intent-qualified at the account level we re not talking about individuals that simply stopped by a booth or viewed a piece of content pipeline quality also increases. As a matter of fact, intent-qualified opportunities tend to be larger, move through the pipeline faster, and convert at a higher rate. WIN-RATES An increased focus on in-market companies most often leads to higher win-rates. Insight into the actual buyers journey, including what companies were researching when, allows companies to better understand what influences sales outcomes. Sales and marketing can then develop battlecards and targeted talk-tracks to increase win-rates. Combined, these benefits lead to significant return on investment ( ROI ) by companies effectively applying intent data to their marketing and sales efforts. Several early adopters of intent data shared internal reports with us showing 3-5x ROI on their intent data programs. 5

6 4. WHAT ARE THE DIFFERENT WAYS TO CAPTURE INTENT DATA? A B C CAPTURING ONLINE BUYER BEHAVIOR First, they must capture the online behavior of B2B buyers. CATEGORIZING CONTENT Second, they need to categorize the content consumed into keywords or topics that are in turn tied to potential purchases. SEPARATING INTENT SIGNAL Third, they have to separate the signal of true intent to purchase from the noise of ongoing activity, especially from companies with large numbers of employees. 6

7 A. CAPTURING ONLINE BUYER BEHAVIOR Intent data is captured in three primary ways: bidstream data; website tags; and registered user websites. The characteristics of the intent data derived from these three approaches are quite distinct and should be understood before companies attempt to apply any of them to their businesses. BIDSTREAM Bidstream data refers to the meta data made available through programmatic advertising platforms. When a user visits a website with programmatic ad inventory, the data provided for automatic bidding includes information that associates the visitor to a company and the page content consumed to keywords relevant to purchase categories. The leading provider in this model is The Big Willow. WEBSITE TAGS Website owners agree to place a javascript tag or pixel on their website and share their visitor data with 3rd parties. The leading provider here is Bombora. REGISTERED USERS Users must register to download information or to access a site s full functionality. Companies may target ads to collections of users (e.g., LinkedIn) or purchase their contact info directly, if the users have knowingly or unknowingly given consent to the website owner to share their contact information with all of the website owner s customers (e.g., Tech Target, G2 Crowd) 7

8 B. CATEGORIZING CONTENT CONSUMED INTO INTENT CATEGORIES Once the data is captured, the next challenge is to categorize the data by keyword or topic to associate it with potential purchases. Each of the three approaches uses a fundamentally different approach to categorize the content consumed. Bidstream takes a bottom-up approach based on keywords in use by vendors. Website tagging uses a top-down approach at a topic level, with each topic reflecting a range of potential keywords. Registered user sites simply use the title of the downloaded document. These different approaches must be understood before companies attempt to apply any of them to their businesses. BIDSTREAM Categorization of web pages is done via Natural Language Processing (NLP) at the keyword level. Each category is defined from the bottom up based on keywords in use by vendors and their competitors. As a result, the vendors in the space define the category and it evolves as the space evolves. WEBSITE TAGS There is no categorization at the keyword level but, instead, top- down definition at topic level. In this case, it s up to the intent data vendor to choose which topics are relevant, without being able to explicitly connect topics to relevant keywords. REGISTERED USER The title of the document downloaded determines the category in which the registrant is placed. No broader visibility into buyer behavior is categorized or provided. 8

9 C. SEPARATING INTENT SIGNAL FROM NOISE Once buyer behavior is captured and categorized, it must be analyzed to distinguish a purchase intent signal from the ongoing noise of online research activity. Each of the three methods takes a different approach to this last challenge as well. BIDSTREAM A company-specific model of activity by keyword on a trailing 6-month basis is created. Current activity is compared to this baseline to determine true spikes in research activity by keyword. WEBSITE TAGS A weekly normal distribution model is created that compares activity across companies at a topic level. Companies with the most activity receive the highest score. The risk in this approach is that large companies will consistently receive a high score and thus generate false positives. REGISTERED USERS No model of intent signal here. Every download is assumed to signal intent. 9

10 5. HOW DO THE INTENT DATA FROM THE THREE METHODS DIFFER? The three methods for capturing intent data result in significantly different intent data outcomes. A comparison of the different characteristics of these three primary methods are listed in the table below: BIDSTREAM WEBSITE TAGS REGISTERED USER REACH: THE WEB Billions of web pages Tens or hundreds of relevant sites for a given product, based on opt-in 1 website or closed community of sites run by one company REACH: GEOGRAPHIC Global coverage today Primarily English-only sites in US and Europe Primarily English-only sites based in the US GRANULARITY OF INSIGHT White Box page visits connected to company, geographic location, page URL, and device Black Box No visibility into underlying data Registration info from download of a piece of content BUYERS JOURNEY Time-series data visualization of actual buyers journey across webpages Black Box No visibility No visibility beyond single download 1 data point SIGNAL FROM NOISE Company-specific baselines by keyword to determine true signal No company-specific models companies with the most activity each week receive highest score No visibility into buyers journey AD TARGETING AT MOMENT OF INTENT RESEARCH Available across 12 billion web pages Not available No intent targeting. Standard display ads on a few websites to all visitors GEOTARGETING Web visitor resolution to zip code + 4 Company location only, web visitor may be anywhere Information provided in form fill or registration 10

11 6. WHAT ARE COMMON PITFALLS WITH INTENT DATA? The value obtained from intent data is limited by the following traps we see companies falling into: CHECKBOX Intent data is treated as just another score, one that gets added to a marketing automation or ABM tool and ignored along with all of the other scores and attributes. If it is selected as a field ( all companies above 70 on a 100 point scale ) in an campaign, for example, no rigorous A/B testing is done to determine the performance improvement provided. Accurate intent data is not just another attribute. It is THE SIGNAL of purchase intent. A checkbox approach never confirms the accuracy of the data and, if it is accurate, never ties the data to a specific, appropriate action. BLACKBOX Many companies grow frustrated with the black box nature of some intent data providers and stop their initiatives. An inability to see the underlying data points that roll up into intent scores is a major challenge for analytic teams that need to test and prove the validity of intent signals. The Big WIllow does provide a white box approach for teams that require such granular-level intent data visibility. FALSE POSITIVES Depending on the chosen intent data approach, false positives can be a significant factor. False positives can lead to a significant loss of time. When working accounts who are not in-market to buy, support from the sales organization can be lost. Companies should evaluate intent data providers for their ability to minimize false positives. ROUND PEG IN SQUARE HOLE Larger companies often have their own internal predictive analytics teams that have built predictive scoring models based on discrete attributes and regression modeling. Intent data is a near-continuous variable that must be modeled against behavior across the internet, not just against one company s data set. This makes it challenging for intent data to be incorporated into existing predictive models, and many companies have given up trying to make it work. SEEN ONE, SEEN THEM ALL Intent data providers vary widely in their capture mechanisms and ability to deliver accurate, actionable data. Companies closely evaluate the alternatives in all solution selections and should do the same with intent data. 11

12 7. WHAT ARE THE RECOMMENDATIONS TO MOVE FORWARD WITH INTENT DATA? Getting started with intent data can be straightforward. Our recommendations are as follows: SKIP THE EXPENSIVE SOFTWARE Many ABM software providers are trying to bundle intent data into a large software and integration commitment. Unfortunately, this is expensive, involves a great deal of effort focused on redoing a martech stack, and takes the focus away from getting value from intent data by driving sales. Companies have enough software. They just need to make their existing software smarter with intent data. Leading companies gain significant competitive advantage from intent data. Several we engaged with for this research have set up CEO-sponsored, corporate-wide initiatives to leverage intent data CONDUCT AN INTENT AUDIT If you are already using an intent data provider, ask another intent provider to provide an audit to compare the intent signals. This will help identify potential false positives or missed opportunities. across their business units. If your company has not begun investigating intent data, now is the time to do so. START WITH A DYNAMIC ACCOUNT TARGETING PROGRAM Select a provider that can deliver an end-to-end solution that doesn t require any software investment or changing your martech stack. Then focus on identifying in-market companies, creating pipelines, and driving wins. LEARN MORE 2018 Aberdeen Aberdeen.com 12