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Advanced Product Analytics, Better Customer Experiences

Mixpanel’s mission is to increase the rate of innovation by helping companies build better products through data. Headquartered in San Francisco, Mixpanel serves over 26,000 companies from different industries around the world.

Read on for their guest contribution to the Iterable blog to learn how to build better customer experiences with advanced product analytics.

As marketers, many of you are judged on your ability to position products while attracting and warming up qualified leads. But without the proper data on how customers get value from their products, you’re left guessing about what customers love most.

Data-led marketers know they need to implement product analytics to drive their product experience forward, but many fail to harness these insights in a productive way. Instead, they get stalled in one stage of analytics maturity, never progressing to a higher level that will improve their customers’ experience of—and affinity with—their product significantly.

But what, exactly, does product analytics maturity mean? At Mixpanel, we think about it in terms of these three elements:

  • Collecting the right data
  • Asking the right questions
  • Using data for cross-functional product decision-making

Product analytics is the foundation for understanding the factors that drive conversion, engagement, and retention—incredibly valuable information for developing a more personalized marketing program.

As you begin to think about the customer journey in a more sophisticated way, you’ll start to ask more complex questions about users, and you’ll need a product analytics solution that can help you answer them. If you neglect to do this, you are limited in your ability to advance the sophistication of your marketing campaigns.

But before you explore what a mature approach to product analytics can help you do, it helps to understand where you currently stand so you can move closer toward your business goals.

What analytics maturity means for your business (and your users)

Having a grasp of your stage of analytics maturity enables you to build a technology stack based on an understanding of your user journey from end to end. Not only will you better understand your users’ journeys, but you’re empowered to act on your knowledge and create deeper relationships with your customers.

“Good marketing is compelling. But great marketing connects with customers on a subsurface level. It’s empathetic and it’s inclusive, and it strums the resonant chords within us.”

~ Garin Hobbs | Director of Deal Strategy, Iterable | StreetFightMag

That’s the ultimate goal of product analytics and marketing analytics working in sync—understanding customers better, and in turn, improving the customer experience over the course of the entire lifecycle.

Product analytics maturity matters because it can help you:

  • Build (and promote) the right things: You can only build products that users will love if you monitor their engagement and levels of satisfaction. With a product analytics tool, you can use data to optimize onboarding flows, re-engage inactive user groups, and identify your power users. As you launch new features or products, you can track how each user cohort responds, helping you build and better promote products over time.
  • Support the critical initiatives: Leverage that same data to remove friction points from your customers’ journeys. Whether it’s onboarding or re-engagement, leaning into product usage data can help guide strategic marketing initiatives and drive engagement.
  • Equip teams to move faster: Customers and their expectations evolve quickly. With real-time user insights, you can enable your marketing team to create campaigns that rapidly respond to consumer trends.
  • Help you get closer to your goals: Enable product and marketing teams to impact key metrics and measure how successfully their activities deliver key customer experiences.

Product analytics for the growth marketer

We all know that no two customers are alike. This is why tools like Iterable exist to help teams communicate and engage with customers in hyper-individualized and relevant ways. As you serve more complex customer segments, you’ll need a more advanced approach to analytics to deliver a more personalized journey.

In order to deliver highly personalized experiences, brands need to:

  • Validate hypotheses about customer differences with data: Every customer is different, and will use your product for different reasons. Use data to validate hunches about the characteristics of various groups—from basic demographic information to more specific criteria, like how they engage with your product.
  • Distinguish core behavior and outcome metrics: Split outcomes and test different messaging flows for engagement and lifecycle throughput.
  • Save cohorts: When you find important splits in your users behavior, record them as various cohorts (e.g. trial user, first time purchaser, recently upgraded) and target each group with specific messages that align to their unique experiences.

Product analytics can help you achieve all of the above. And while it can seem overwhelming at first, it doesn’t have to be.

“Because our product and marketing are omnichannel, it’s really easy to see the entire journey a user takes—from acquisition to moving through one of our hundreds of funnels, to ultimately purchasing a policy.”

~ Gil Sadis | VP of Product | Lemonade

When your marketing platform is connected with your product analytics tool, you can see how customers are not only moving from end-to-end, but also the levels of influence your messaging has on that movement.

Companies that use advanced solutions in other areas of their work—i.e. advanced personalization—will find that they don’t have to crawl through novice stages. They can bridge the gap quickly by implementing the right tool, asking the right questions, and using data for cross-functional decision making.

So, what does analytics maturity look like in practice?

Mixpanel breaks down analytics maturity into five stages ranging from non-existent to expert. Again, these stages are broken down into consecutive—but not necessarily sequential—order.

Stage 0-1: Companies new to data adoption

In this stage, companies have little to no product analytics capabilities. They make decisions based on something other than product data—limited customer feedback, intuition, or assumptions about user behavior. As a result, marketers are likely only scratching the surface of their campaign personalization potential.

For example, a company that is implementing personalization in their marketing tactics for the first time is likely to be in the reactive personalization stage. If you’re in this stage, you can implement simple tactics—like using basic customer attributes (i.e. names, birthdays, or system events), but you can’t do further targeting.

Questions asked in this stage:

  • What behaviors lead users to purchase or to refer a friend?
  • What acquisition channels are driving traffic?
  • What’s my conversion rate to the product?
  • How can I use campaigns to solicit these similar data points in a non-invasive way?

Acquisition questions are a good starting point, but they don’t give you insight into customer engagement or retention. Without those insights, you’ll have major blindspots on the level of your customer loyalty—a key indicator of a business’s long-term viability. This is why marketers (and their cross-departmental colleagues) need to implement something more than Google Analytics.

For example, a new ridesharing app just launched and they are still in this novice stage. They may have a non-existent or small customer base with very little customer feedback. The most data they have is likely related to their website, but otherwise they don’t know how their product is being used. They will need to start asking questions to move on to the next stage. Once they define the questions they want to answer, they can start collecting data and putting it to use.

Stage 2-3: Data is driving decisions and greater customization

In this intermediate stage of analytics maturity, you have a better sense of what questions you want to answer beyond acquisition-related factors and are willing to put the resources into discovering answers. However, you aren’t completely established in your data collection and analysis process.

When it comes to personalization, teams in this stage are in the proactive personalization stage. At this point, you combine user and event data with externally managed data. You can send messages to smaller, targeted segments that are based on customer behaviors and third-party data (e.g. geo-specific or personal interest).

Questions asked in this stage:

  • Where in my funnel do users drop off?
  • What are the most common user journeys?
  • What sequences of actions lead my users to upgrade their membership?
  • What campaigns must we create or improve to further influence action within the product?

These are foundational questions that can only be answered by integrating your customer lifecycle marketing data with your product analytics data. Once you’re able to answer these, you can start to adjust your marketing campaigns with what you see in-app, or on your website.

In this stage, it’s common for businesses to realize they need some additional solution to their Business Intelligence (BI) tool. Even though teams are using data and asking more sophisticated questions, they can’t answer them fully or turn data into productive action.

Back to our ridesharing app. The app has now gained customers and knows that data will guide its product development, but they’re still hazy on which tools to invest in. If they’re committed to using accurate data, they’ll need to ask more of their analytics tool and process.

Stage 4-5: Unified goals and advanced tooling

In the advanced and expert stages of product analytics, you can assess what’s happening to your product, and identify patterns related to these events. At the highest level (“expert”), no decision is made without the use of product analytics.

With this level of attention put towards analytics, you can start implementing individualization. Individualization is formed from dynamic relationships between user attributes and detailed product data. This one-to-one approach is based on profile, behavioral, lifecycle data points to create user-specific dynamic content experiences that are unique to each user.

Questions asked in this stage:

  • How do marketing launches cause changes in specific user behaviors or my primary metrics?
  • How do all my A/B tests affect key metrics over time, and which of them are worth continuing?
  • How many times do users repeat a given step in a funnel before moving on to the next step, and how does that affect conversion rates?

In this stage, businesses use multiple user journey solutions. When it comes to team member ownership—analytics in this stage is an all hands on deck process. Every team member knows their role in using the analytics tool, and uses it regularly.

For example, you can set up a personalization campaign using a growth marketing platform like Iterable, then push the data associated with in-app interactions to Mixpanel to answer those critical questions. By integrating advanced, customer-oriented platforms, marketing and product decisions are both data-based and closely aligned.

Our ridesharing app has now got their tools and systems in place. They’ve invested in a product analytics tool that is reliable, scalable, and can answer endless questions about their customers. The tool is helpful for marketing and product teams alike, giving their customers awesome experiences from start to end.

But it doesn’t stop here. It takes work to continuously stay an “expert” at product analytics and individualization. Teams must stay up-to-date on the product analytics tool and new team members must be onboarded properly.

Implementing product analytics and individualization at scale is a continuous process, and as your product launches new features or services, you’ll need team members to continuously ask the right questions, know how to collect the data, and make future decisions together.

“…Getting someone to register is only half the battle. Do our users continue to engage? Do they complete the onboarding process? Do they bail and delete their accounts. We’ve instrumented in these key elements of participation in Mixpanel, which means that for each landing page, we can see how many people complete actions that correlate with being longer-term users…This information helps our product and marketing teams understand which campaigns are, in fact, working the best to drive users that will continue using Personal Capital for the long run.”

~ Vince Maniago | VP of Product Management | Personal Capital

Understanding where you’re at to understand customers better

“No company is too small to prepare to collect, organize, and analyze data — it’s the foundation of building great products and accelerating your growth in product analytics maturity.”

Brands want to build intimate relationships with their customers. But just like one-to-one relationships between friends, brands need to understand their customers. They need to understand their likes and dislikes, needs, passions, and behaviors.

There’s no way to do that at scale without accurate data—and a way to make sense of it all. This is why product analytics are a marketer’s best friend. And guess what? You don’t even have to be a data scientist to use one. Mixpanel, for example, allows users without technical expertise to self-serve answers to their questions about users.

There’s no “right” time to start implementing product analytics. But the sooner you get started, the sooner you can start harnessing data to build experiences that customers love.

To learn more about product analytics maturity, read Mixpanel’s new guide: Advance Your Product Analytics Strategy.

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