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Marketing Strategies: How software analytics enables real-time customer personalization

Keith FenechBy Keith Fenech

At its inaugural Think2018 conference held recently in Las Vegas, IBM made its foray into the smart assistant market with a version of its Watson technology called “Watson Assistant.” The technology, according to IBM, will be the first truly intelligent AI assistant, learning through every human interaction to make uniquely personalized recommendations to its users.

IBM shared the familiar example of taking a business trip to Las Vegas. Upon landing at McCarran International Airport, Watson Assistant automatically checks into your hotel and tells your rental car where you’re going. When you arrive, you skip the lines, open your room with an electronic key on your device, and come into a space that has been prepped with your preferences for temperature and lighting, and it is even crooning tunes from your playlist.

Aside from the personalization the product enables, IBM offers strong clues as to how strategic personalization is in the way it is selling the product itself. Watson Assistant won’t be sold in stores or marketed to consumers, but to B2B clients, in a model that will allow vendors to include their own brand voice and use cases, such that the consumer will be communicating not with Watson, but with the brand of the rental fleet or hotel itself.

If there was any doubt that deep personalization strategies are crucial to each and every angle of product development and delivery, our dear Watson (and Hey Google, Alexa, Cortana, Siri, etc.) erased it.

The more interesting question is how to implement personalization in the context of your software product strategy – especially the types of personalization strategies that can be leveraged in a cross-organizational manner. Even the most robust and thoughtful combination of email, market research, user surveys and personal outreach can fall short of what today’s business really demands. But as organizations seek compliance with General Data Protection Regulation (GDPR) mandates and must adjust traditional marketing strategies, personalization becomes even more challenging.

Achieving this type of segmentation is entirely possible in product development and delivery – and doing so in a manner that is compliant with statutory and regulatory mandates such as GDPR isn’t as difficult as it may seem. Dynamic in-app messaging driven by software usage analytics offers a compelling solution by remaining anonymous yet still highly targeted – by leveraging actionable data that is not personally identifiable.

Powerful Segmentation Drives Real-Time Personalization

Let’s consider a familiar example.

As engineering works on new functionality for an upcoming release, the plans to sunset an existing module start to draw the attention of sale reps, who insist that customers in key accounts will not upgrade to the new edition if this particular feature isn’t included. Product management knows, via usage intelligence, that this isn’t the entire story. Analysis of runtime metrics has shown that there are indeed super users who are using this particular module – but the majority are not.

While the data is accurate, it is still important to leverage it to build consensus.

The product manager can now pull together key stakeholders from the marketing and sales organizations and propose a solution: Let’s reach out to those super users, with targeted in-app messaging, and start to educate them on the new functionality while they’re engaged with the product. We can base these messages on the behavioral and environmental data gathered from our usage intelligence.

We’ll use this as a channel for deeper interaction, by asking for contact information and time to discuss some of the new features. We’ll dedicate engineering resources to further supporting the solution if that emerges as the preferred option, but let’s start by showing them what’s new and what’s next.

In such a manner, some of the potential biggest critics of the new release can become its biggest advocates – eagerly weighing in on and adopting new features, taking to user forums to serve as experts and really changing the release trajectory altogether.

Deep personalization strategies are crucial to each and every angle of product development and delivery. Photo Courtesy of

Deep personalization strategies are crucial to each and every angle of product development and delivery. Photo Courtesy of

Segmentation + Context = Engagement

In such a way, dynamic, data-driven in-app messaging can become the ultimate tool in your personalization toolkit, bringing cross-organizational benefits few approaches can accomplish.

Product managers can use contextually relevant in-app messaging to conduct targeted surveys or solicit feature feedback from actively engaged users, as well as educate them on new functions. This can help find answers to specific workflow questions and product design queries.

For marketing and sales, deploying this type of personalized in-app messaging during freemium promotions can more quickly turn prospects into paying customers. Sales teams can improve customer retention by timing offers with periods of increased key-feature activity, overtly referencing the features used most.

For software developers, usage analytics lends unprecedented visibility into how their product is being used, by real users, in real time. By pairing in-app feedback surveys with usage analytics, development teams have a proven method to gain clarity on why a feature they’d like to sunset is considered a “must-have” by devoted users.

Teams can get quick, inline input during beta testing from the users most qualified to provide it without having to rely on an external bug tracking system. In the case of new builds to tackle bug fixes, support can proactively reach out (within the application) only to those users who are directly affected and point them to the remedy.

Software usage analytics also holds value for license compliance and risk management purposes. Messaging license abusers or pirates “in the act” eliminates any ambiguity of usage, and it can ease the process of revenue recovery. On the flip side, confronting the sad reality that data breaches are becoming more likely (coupled with new GDPR requirements that accelerate notification procedures) means every organization must innovate its approach to risk management. Some of the world’s biggest retail brands have leveraged in-app messaging to notify users of data breaches.

An in-app messaging strategy rooted in software usage analytics can help your team imagine, develop, build, market, sell, encourage adoption and continually optimize products that are deeply in tune with user expectations, and perhaps expand expectations they don’t even know they have yet. Data-driven in-app messaging builds on the trend that we can leverage technology to learn and deliver more to our users. It’s a different sort of AI, perhaps authentic intelligence, that can help us build, sell, and help our users continue to optimize products that are truly reflective of their business needs.

Keith Fenech ( is vice president of software analytics at Revulytics. He was co-founder and CEO of Trackerbird Software Analytics before the company was acquired by Revulytics in 2016. Prior to founding Trackerbird, Fenech held senior product roles at GFI Software where he was responsible for the product roadmap and revenue growth for various security products in the company’s portfolio. He also has 10 years of IT consultancy experience in the SMB space.

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