How to Leverage Product Data for Growth and Retention in 20218th Feb 2021
Marketing comes down in large part to a series of decisions: Who do you target? When do you target them? What do you share to attract their attention? How do you keep them engaged and get them to convert?
Traditionally, the answers to these questions came from a combination of gut instinct and experience. In 2021, that doesn’t cut it. Data is now everywhere and readily available for marketers to use, and doing exactly that will be what sets apart the most successful organizations from the pack.
Companies that leverage customer behavior data outperform their peers by 85% in sales growth and more than 25% in gross margin.
Research from McKinsey & Company
In the SaaS world, in particular, using product data to drive growth will prove critical to success in 2021. According to McKinsey & Company, “companies that leverage customer behavior data outperform their peers by 85% in sales growth and more than 25% in gross margin.” Data from Aberdeen Group echoes this point, finding that organizations using analytics data embedded within business functions like marketing and sales are 22% more likely to see an improvement in the speed of decision making and report significant growth in average deal size, renewal rates and upsell/cross-sell opportunities.
Of course while the benefits of using product data to get smarter about scaling the business, targeting the right people at the right time, and better understanding what’s working and what’s not are clear, there’s a reason not all marketing teams already do this. Quite simply, it’s proved challenging in the past.
This challenge stems from the fact that product data usually gets “gated” behind tools that marketers don’t really use (like data warehouses), which means they need support from someone like an engineer to get that data (yes, even in digitally-native SaaS companies). And that process not only involves several layers and extra steps, but it also takes time -- including submitting a request, getting the time slotted into the engineer’s busy schedule, actually pulling the data, and then sending it back to marketing to get it into systems that can use it. A recent IDC report finds that 33% of workers believe they waste too much time preparing data (which involves getting data from an average of six sources) and 29% suffer from slow response times to requests for data. In the course of all that, it’s very likely the data can become outdated.
While this challenge is very real, it’s not a dealbreaker by any means. The benefits of using product data in SaaS marketing far outweigh the challenges, and you can even eliminate many of those challenges with the right setup. Here’s what you need to know.
This challenge stems from the fact that product data usually gets “gated” behind tools that marketers don’t really use (like data warehouses), which means they need support from someone like an engineer to get that data.
What is product data, exactly?
First thing’s first, what’s all the fuss about product data? What exactly are we talking about?
Product data (also referred to as behavioral data or customer behavior data) is information pertaining to a customer’s interactions with your company’s product or app. Importantly, this information can vary based on the specific actions your company wants prospects and customers to take.
In general, product data can help answer questions and provide information like:
- Which users have created accounts for our product
- Which users have upgraded their accounts?
- Which users have viewed “critical” pages, like pricing, account subscription, etc?
- How often do certain users log in to the product?
- How much time do certain users spend in the product each day?
- In what areas of the product do users spend the most time?
- Are there any areas of the product where users tend to get stuck?
- How many different people from the same team have created accounts?
- Which users have invited other team members to create accounts?
- Have users integrated the product with any other applications or their own website?
- Which users based on product engagement are the most valuable customers long term?
- Which users based on product engagement are most likely to upgrade their account?
When it comes to using product data, the options are plentiful depending on the types of questions your team wants to answer based on your goals. That’s because every time a user takes action on your site and in your product, they create a trail of data that provides detailed information about their needs. So whether you want to improve acquisition and conversion among prospects who sign up for a freemium version of your product, increase retention among existing customers, or anything else, gaining access to the mountain of product data sitting in your organization can provide the necessary insight to get more targeted with those campaigns.
Who should use product data?
The options to use product data to power more targeted marketing are limitless, but there are certain types of companies and teams that have the most to gain from taking this approach.
Specifically, SaaS and tech companies have the benefit of being born digitally, where everything can be captured and tracked. That situation puts these organizations in prime position to take advantage of all this data since it’s likely already getting collected (from there it’s a matter of ensuring customer-facing teams have easy access to that data).
Taking it one step further, SaaS companies that offer a free trial or a freemium model are especially well-positioned to use product data due to their two-step acquisition cycle:
- Acquiring users for the free trial/freemium account to get them into the product and using certain elements of it
- Converting those trial users to full-time, paying customers to gain ongoing access to the product and the ability to take advantage of more features and capabilities
Product data is extremely useful in this scenario because it can help you hone in on (a) the right trial customers to target based on usage (e.g. those who have invited others, spend a lot of time in the product, visit the pricing page) and (b) identify the best ways to target those customers based on the features they’ve already used or those you know they’ve tried to use but can’t because of pay gates in the trial/freemium version.
Why is product data so valuable?
It’s true: Just because your company collects all of this product data doesn’t mean it’s readily accessible to your marketing team. In most cases, it’s not readily accessible and making it easy for marketing to access and use is challenging, but absolutely worth it.
Mixpanel sums up the value of product data well, noting that the closest a company can get to having customers on their own team is analyzing product usage data. Why is this the case?
Traditional marketing focuses on targeting by what someone looks like on paper, such as their job title. But that information isn’t a good indicator of buyer behavior. It doesn’t tell you if that person is ready to buy at a certain time or interested in achieving certain goals; it only gives you a general idea of what they do in their company, and even that can vary from one organization to the next.
Using product data is a far better indicator of buyer behavior and allows your marketing team to take targeting efforts to the next level by generating insights based on what someone is actually doing (or telling you they want to do with their actions) -- not what you think they should be doing.
Todd Yellin, VP of Product and Innovation at Netflix, describes this fallacy best: “It really doesn't matter if you are a 60-year-old woman or a 20-year-old man because a 20-year-old man can watch Say Yes To The Dress and a 60-year-old woman could watch Hellboy.”
Importantly, this type of product data is both objective and specific. While you can try to get this type of information from other methods, like interviews, those methods generally lead to more subjective and vague information. Direct product data will always be accurate and unbiased while also providing the level of specificity needed to make it actionable and reliable.
A SaaS marketer grounds this to apply to her own team, asking: “How can we know who to target, appropriate messaging, features to highlight, or campaigns to develop without an understanding of the product actions that drive long-term retention?”
She notes that she came to this conclusion after seeing product data like which features customers engaged with, how often they logged in, the in-app messages they clicked on, and what steps within the product led users to become customers. All of that data got her wheels turning about opportunities to create targeted marketing campaigns with video instructions, in-app guidance, and tutorials to make it as easy as possible for customers to take those desired actions in the product.
The possibilities are truly endless, as using product data not only helps set new goals that can grow the business (like converting more free customers to paid customers and growing revenue and loyalty from existing paid customers), but it also provides the necessary insight to create more targeted campaigns to reach customers in pursuit of those goals.
It really doesn't matter if you are a 60-year-old woman or a 20-year-old man because a 20-year-old man can watch Say Yes To The Dress and a 60-year-old woman could watch Hellboy.
Todd Yellin, VP of Product and Innovation at Netflix
How can you make product data actionable and use it effectively in your own marketing campaigns?
If you're sold on the “what” and the “why” behind using product data in your go-to-market strategy and you know that data already exists within your company, how do you actually make it happen? This, of course, is the million dollar question, since it’s one thing to say what you want to do and it’s an entirely different thing to make that vision a reality.
Your strategy for using product data will depend on what you want to do with it. A recent Forbes Tech Council article advises: “It's worth asking multiple business units what they might want to know in the future because you can't answer questions with data you didn't collect. After you've created a thorough list of questions you'd like answers to, the next step is to ask, "What kind of data will I need to answer these?"
Generally, we can bucket go-to-market strategies based around product data into three key goals:
- Marketing-led growth and acquisition: Make product data available so your marketing team can use it as part of a lead scoring model or even as a basis for qualifying leads. For example, you can tie score values to different actions customers might take in the product and even enrich that data with company profile information to ensure the best fit. Ultimately, this approach will leave you with Product Qualified Leads (PQLs), which can help your sales team identify the best accounts to talk to (in part by reducing the amount of accounts by disqualifying certain ones based on poor product usage fit) and increase account value by up to 30%.
- Sales-led growth and acquisition: Make product data available so your sales team has access to that information in the tools they use daily, like CRM and chat. For example, you can track certain product usage signals and then automatically notify sales (e.g. via chat) when accounts fall into unique segments like newly engaged leads or those at risk of churning. This type of trigger can help sales teams react faster to reach customers with pertinent information at a more relevant point in time based on their actions.
- Retention among existing customers: Focus on product data from existing customers to help marketing and customer-facing teams increase retention. For example, you might look at what actions within the product lead to the most valuable customers (as defined by overall product usage, long term loyalty, or anything else) and then create campaigns to drive more users to take those actions. You can also use data like a drop off in log ins and overall usage to identify potential churn risks earlier on. On the flip side, you can use data like visits to gated features to identify potential upsell opportunities.
Once you identify your goals, you can determine how you want to use product data within campaigns to achieve them. From there, you should have an understanding of the data you’ll need to power those campaigns and can work on getting the product data out from wherever it’s housed.
Making this data available for your marketing and sales teams hasn’t always been easy. It’s often involved requesting support from already-strapped engineering or IT teams to just get it in the right systems and then work on top of that to make it usable in campaigns. However, this doesn’t have to be the case. In fact, the right customer data platform (CDP) can make this entire process quick and easy.
For example, Hull offers pre-built integrations to data warehouses (including PostgreSQL, Snowflake, and BigQuery) that allow for easy data extraction and ingestion out of the box.
Watch this 8-minute video clip from our "Your Data, Your Way" webinar series of Hull Product Owner, Michal Raczka, showing a demo of our SQL Importer ingesting data from Postgres.
Once the integration is in place, the opportunities for using that data open up. Consider the following use cases, which are just a small sampling of everything that’s possible:
- Define your ideal customer profile with a variety of data for a more well-rounded view of potential customers. Instead of relying solely on company and person-based attributes, bring in more detailed data from a variety of sources, including product data, billing data, social media data and more. This approach can help your team better understand what your ideal customers look like and then prioritize leads accordingly. It even helped one organization achieve a 30% reply rate on cold outreach.
- Better qualify and prioritize leads by supplementing attribute-based data with behavioral data to paint a clearer picture of who may be more likely to buy your product. Beyond just knowing who you want to go after by creating an ideal customer profile, this approach can help you apply that knowledge based on what actions your leads actually take at any point in time. Armed with this data, you can start to do things like filter and stack-rank leads so that your sales team can more effectively prioritize their outreach based on buying signals.
- Enrich CRM and marketing automation data with valuable points on how customers and prospects have interacted with your product. This enrichment allows you to do things like compute new attributes and log key behaviors or milestones reached. In turn, it can deliver key benefits like an improved ability to experiment with data-based campaigns and access to more complete and recent data to power highly targeted marketing campaigns.
- Personalize email campaigns based on customer behavior, such as tailoring follow up emails to customers based on which web pages they visit or actions they take in your product. This personalization makes it possible to create a more relevant experience for customers based on what they tell you they’re actually interested in through the actions they take, rather than your team making assumptions based on demographic or firmographic data. As a result, it can not only lead to higher rates of engagement, but it can also help drive prospects or customers to take desired actions more efficiently.
- Visualize the customer journey to better understand the common paths that your customers take and develop new campaigns accordingly. According to Gartner, “mapping customer journeys uncovers opportunities by understanding customer expectations and unmet needs, as well as identifying gaps and opportunities that are an essential part of the customer experience.” Specifically, this approach can help you create more nuanced streams of communications for different types of customers and even help you target those customers at key points in time, for example when they reach certain milestones that make them more likely to be interested in an upsell or when they a hit a certain area that tends to give customers pause and need a little extra “unsticking.”
Ready to incorporate product data in your go-to-market strategy?
The benefits of incorporating product data into your go-to-market strategy are undeniable: It can help increase revenue and retention through better, more targeted customer experiences that reduce common points of friction. It can also help your sales and marketing team better qualify and prioritize leads to achieve those goals more efficiently. And those benefits are just the tip of the iceberg.
In 2021, the question is not should you incorporate this type of product data into your strategy, but more about can you do so and, if so, how? Realizing these benefits requires providing go-to-market teams with easy and timely access to this data to make key decisions and take action accordingly. This outcome requires investing in technology like a CDP.
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Angela brings over a decade of B2B technology marketing experience to her role as the Director of Marketing at Hull. Prior to Hull, she spent 5 years at utility data aggregator, Urjanet, where she held various roles in demand generation, marketing operations, and product marketing.