TIMING IS EVERYTHING

The Case for Embedding Analytics in Your Product Sooner Rather Than Later

You love your product.

And your dream for your product is a wildly successful launch, soaring rates of user adoption, and customers who sing its praises from the rooftops. You want reviews of your product to read like love letters, for your product to become indispensable to your end users, to become part of the DNA of their daily lives. You want your future customers to love your product as much as you do.

Timing Is Everything Love Your Product with Analytics

Regardless of your industry, the customers of today want and expect that their products deliver one critical element on top of your product’s base functionality.

They want their data. They want to see with their own eyes, independently, how much they use the product, and how well they are benefitting from it.

And they want their data to tell them what to do next.

“We are drowning in information while starving for wisdom. The world henceforth will be run by synthesizers, people able to put together the right information at the right time, think critically about it, and make important choices wisely. ” -E. O. Wilson

The Difference Between Good Products and Great Products:

Timing Is Everything Good Products vs. Great Products

What Makes These Great Products So Great?

1. They give people what they want - their data!

2. They deliver data in a way that empowers customers to make decisions - for their business, for their team, for their health, for their finances, etc.

The corporate manager can use her project management software to identify which departments require a higher touchpoint with her own department and make decisions about staffing to achieve business goals.

The doctor can identify which physician’s assistants have a higher rate of incorrect first diagnosis. He can then drill down in the data to investigate whether this data correlates to seasonal increases in traffic when the clinic is understaffed, or whether it simply relates to poor decision-making.

The marketing agency CEO can instantly identify the account managers with the highest rates of customer satisfaction and lowest rates of turnover, and task them to lead a series of workshops for under-performing AEs on the strategies they use to keep their clients happy.

The HR team can instantly identify which managers are associated with the highest rates of employee turnover, and make a critical business decision to replace certain management roles and/or hire an organizational consultant to mentor managers in the company during their first year in the role.

The transportation logistics manager can easily visualize data on which drivers are most efficient, whether underperformance can be attributed to an inefficient route or poor performance, and identify which routes should be canceled or adjusted to achieve maximum ROI.

“By 2020, 50% of organizations will reject solutions from new vendors that contractually inhibit their ability to extract their own data.” (1)

Why Embedding Early Feels Scary—and Why It Shouldn’t

For the majority of early-stage software companies, embedding analytics feels like a far-off decision. If you’re aiming to model those who came before you, embedding analytics isn’t something you consider before your product is packaged and marketable, before it’s undergone rigorous beta-testing, and before customers are paying for it.

Embedding analytics any earlier—for the past two decades—has been framed as a risky investment. Why? Because embedding analytics requires a Business Intelligence (BI) partner, and that partner comes at a cost many software companies aren’t willing to make before they’re profitable.

But modern, data-savvy customers no longer view data (and the decisions it can fuel) as an added perk. They want products with analytics insights out of the box. Your well-established competition may have waited 3 years to adopt an analytics component in their product. But now that they have, that functionality is standard. And if you want to stand up, compete, and beat the existing solutions, you can’t follow the old timeline. You have to give the people what they want from Day 1.

More than ever, embedding analytics is a standard cost of doing business. You must budget for a solution before you have customers because analytics will:

  1. Set you apart from other early-stage companies too risk-averse to make an early decision

  2. Set you on par with well-established industry leaders faster than the average early-stage company (because you dared to frame the decision not as a risk, but as a vital and inescapable investment in your product)

Toppling the Old Paradigm on When to Invest in Embedded Analytics

Back when embedded analytics was a solution considered for proven products years after launching, these solutions were framed with a key set of KPIs in mind.

Timing Is Everything Old Paradigms

Embedding analytics was viewed by established product providers as a solution that would:

  1. Build differentiation in a market that wasn’t yet defined by analytics as a standard feature.

  2. Land new business by converting customers away from current solutions that hadn’t yet adopted an embedded solution—and increase revenue on current customers with upsell campaigns highlighting the new analytics features.

  3. Increase customer satisfaction by providing analytics only replicable by:

    +A nearly identical offer from one of the few other software packages or products that offer analytics—which requires a time-consuming migration process

    +Allocating in-house talent and resources to an in-house analytics solution—time better spent improving the core product

    +Manual, half-integrated, and patchwork analytics and reporting solutions that save money only when ignoring the labor required to do them well on a consistent basis

  4. Increase revenue by:

    + Including the analytics solution in your core offer and raising prices on all existing customers

    + Upselling existing customers on an additional analytics feature

    **Both requiring time and money to educate clients on why they should care enough about analytics to pay more now, rather than using the feature as a selling point in the initial sale.

  5. Accelerate thought leadership and mindsharing by providing your customers and your own marketing team with the digestible data visualizations and actionable insights necessary to create case studies that will help potential customers sell themselves on your product.

  6. Reduce costs by skipping the requisite 2-3 years of patchwork analytics reporting your early customers will demand much sooner than you think.

But as customer expectations shift—with data and actionable insights now a standard feature of all products and software —let’s take a look at how modern companies must approach their analytics timeline with competition, revenue, and customer satisfaction in mind:

Timing Is Everything New Paradigms

The Benefits of Embedding Early at Each Stage of the Product Development Journey

As your product comes to life, there are 3 early stages in which you can choose to embed analytics to stay ahead of the game. The benefits of embedding during one of these early stages are unique to the stage itself. And while development timelines are fluid and vary by team size, financial resources, and access to talented leadership, the 3 core stages can be broken down by:

  1. The Development Stage: your product is still under construction.

  2. The Beta Testing Stage: you’ve packaged your completed product, and it’s currently being tested by key users at no cost or reduced cost.

  3. The Early Customer-Base Stage: you have a small number of early adopters and a product that can’t yet handle large amounts of data or display it visually to your customers.

Let's explore the benefits of embedding early per stage of your product development journey.

1. The Development Stage

Embedding analytics at the earliest stage of the product development journey provides the greatest number of benefits in terms of customer satisfaction, report distribution, ROI, and revenue.

Rather than waiting for your early customers to demand detailed reporting, you will provide it up front, either included in a higher-priced base offer or an add-on package. Instead of wasting years patching together analytics solutions for your antsy customers—in which the money you save on a cheaper platform is allocated to cover the time and resources necessary to generate patchwork reports—your product's embedded analytics will do the reporting for you, making your customers completely self-sufficient.

And best of all, instead of a lengthy embedded analytics adoption process to centralize all your disparate data sources and reports after 3 years of winging it—you’ll be tracking every piece of customer data from your very first sale.

2. The Beta Testing Stage

“Value is not determined by those who set the price. Value is determined by those who choose to pay it.”
― Simon Sinek

Embedding analytics in your product during the beta testing stage is the perfect opportunity to gauge:

  1. How your future customers will value access to their data—and price your product accordingly.
  2. How your future customers will use the analytics you provide—and streamline your dashboards, filters, and hierarchies so your customers enjoy maximum BI value.

Through gleaning this information from your beta users, you’ll also be well poised to market your analytics functionality as a differentiator amongst other early-stage competitors in your industry. Ask your beta users what they love and why. And hit the ground running when it’s time to launch your product to the world.

3. The Early Customer-Base Stage

You have an early customer base—and they’re demanding higher quality reporting and access to their data. Your embedded analytics adoption process will be lengthier than it would be had you embedded in the development or beta testing stage, but you’re still ahead of the game. This is a great time to start looking for ways to streamline your offer and operational costs by embedding analytics.

Let’s take a customer example:

A growing CDN (Content Delivery Network) supplier has been using patchwork reporting solutions for years to avoid partnering with a BI vendor for embedded analytics. But when they do their due diligence to calculate the true cost of this patchwork solution, they discover that with monthly outsourcing costs to generate reports and multiple basic analytics platforms to fuel those reports, they’re spending $15K per month to distribute reports to their customers. When they laid it all out on the table, the requested price tag for embedded analytics supplied by a BI vendor felt like a decision that should have been made years ago.

Final Thoughts

Product providers continue to embed analytics solutions at all stages of the product life cycle - it’s never too late to provide your customers with the immense benefits of BI within your product.

However, as stand-alone analytics apps fall out of favor and embedded analytics solutions gain popularity for their seamless, agile integration with all data types and sources, there’s no longer a reason to:

  1. Wait years to offer your customers analytics and data from your product.
  2. Have your customers suffer years of frustrating and incomplete analytics and reporting from your product.

“The cloud has enabled analytics to offer greater value as integrated, embedded applications that provide contextual data within workflows for deeper insight. Based on trends in the Value Matrix and end-user feedback, we’re calling it now. Stand-alone, ‘best-of-breed’ analytics apps will be history by the end of 2019.” -Ian Campbell, CEO of Nucleus Research (2)

To maximize customer satisfaction, increase revenue, compete with the giants, and streamline reporting distribution for the sake of sanity, more and more product providers are choosing to embed analytics in their product at the earliest stages of development.

Ready to learn more about embedded analytics?

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