We’ve all been at this analytics thing for a while now, and with all the advances in big data and algorithms and data visualization it’s sometimes too easy to lose sight of some of the bedrock disciplines that got us here.
Big companies are different from smaller ones and the stakes are higher where mistakes are made. Getting these three basics in good working order at the enterprise level makes positive change much more likely than not.
Who’s in charge here?
Many large organizations have content owners across the country or across the globe, and they are often in different stages of analytics maturity. Each team seems to think they have found a solution that works (more or less), but when it comes time to compare a Golden Delicious to a Granny Smith, often enough it turns out your team in Kalamazoo is using a pomegranate. You need to standardize on a platform and make it your benchmark. Each platform measures things in different ways and there is no reliable way to compare (for instance) what a “visit” is in one platform versus another. Most of the time, they won’t match.
The enterprise may also engage a fair number of content-creators, ranging from full-service agencies to bloggers to pure-play development shops. Too many of them also claim they can do the measurement as well as the creation. Avoid this trap by centralizing measurement inside one team that’s responsible for standards, governance, and measurement itself. It may require bringing in a digital analytics consulting team that can laser-focus on these issues. The alternative is a world of rogue sites, unhelpful self-measurement, and no way to roll up results beyond the market level.
2. Data Integrity
The notion of data collection is often remote and mysterious to marketers and decision-makers. They have little knowledge of the details and less control of it. Safety seems to require skepticism about accuracy – and this leads rapidly to nothing at all. Because if you cannot trust the integrity of the data, you won’t be taking any action based on it.
Taming this problem requires standardization (as noted above), but also trust in your analytics team.
Trust is a nice word, but how about verification?
A tag audit is a great place to start. Find someone neutral on content and measurement and have them look at whether the data is being collected properly. Too often, the answers will be less comforting than you’d hoped, but knowing is the first step toward fixing. Are tags firing more than once on a page? Are certain things not tagged at all? Are parameters set correctly so that the data maps to actual reports? Audit today.
3. Content Actionability
The main reason you measure user activity is to find out whether your content is working. This implies that if it turns out the content is weak, it will need to be changed. And how will that happen? Do you have a plan, a process, a regime that addresses this key element to success? Or do you end up making recommendations that never see the light of day? Are politics getting in the way? Is someone’s favorite campaign or favorite agency looking not-so-good after measurement? It’s tough, but this kind of thinking needs to be defeated before you can win at optimization.
Actionability requires human intervention. It requires putting aside personal preference and prejudice and looking at the data. Your mindset needs to be grounded in success metrics rather than aesthetics or alliances. If you want more customer activity and a healthier bottom line, ditch the so-called loyalty to any particular campaign or site design and look at its performance. The race goes to the swiftest car, not the prettiest and certainly not anyone’s subjective “favorite.”
Simply put, you need to have a plan to govern your data collection, then verify its accuracy, then be confident enough to make changes based on what you find out. If you are thinking this sounds pretty hard, you won’t be the first.
But making a success of it brings success to the business. Take immediate steps to fix these problems and you will be glad you did.
The Internet of Things is all the rage with marketers today, and for good reason. Connecting to customers everywhere lets us marketers deliver more consistent and meaningful experiences. But behind the scenes, mobile apps, the lifeblood of these devices, operate at a fundamentally different pace than the traditional Web.
With the Web, tag management systems have become de-facto tools for enabling marketing agility and eliminating dependencies on IT cycles. But for many brands, marketers find themselves bound to development cycles all over again in the world of apps. By definition, apps are “compiled,” meaning they are hard-coded just like websites of the old days. When marketers want to extend technologies on mobile apps, they must typically rely on app developers to hard code each technology’s software development kit (SDK). With this very manual SDK approach, marketers have to wait for app developers to compile the app, wait for the app store to accept the new or revised app, and then wait for consumers to download an app update. Unlike today’s Web, marketing through the Internet of Things is hardly agile.
The Internet of Things represents an additional challenge as each “thing,” or device, is both a consumer of information and an originator. Our phones act more like devices and our devices act more like phones. Devices range from TV sets to mobile phone and computers; from clothing or wearables like Fitbit or Google Glass, to something movable – your car, plane, bike, tennis racket, and toothbrush that all report collected data from the device. So how can a marketer leverage this data for better marketing everywhere, and deliver more meaningful experiences on these “things” in the first place?
While I’ve noted the aggressive move to mobile in the last 12 months by many leading companies, some companies are technically making this all work, and they’ve been able to remain agile even in compiled mobile app environments. Companies with a forward-leaning omnichannel vision, such as Getty, Vivint, and United Airlines, to name a few are taking a “real-time” and a “no-SDK” approach to their apps and delivering excellent omnichannel experiences for their users. In other words, their apps are making a tangible impact on customer conversion, engagement, and loyalty — and they’re not slowed down by old-fashioned app development processes. So, in the static world of apps, how are they able to succeed where others struggle?
Ensighten chief executive (CEO) Josh Manion notes that delivering optimized omnichannel experiences is at the heart of today’s marketing. Marketers must insist on agility in any environment, including native mobile app environments. “For a marketer to settle for anything less does a disservice to both marketers and their customers. The ‘Internet of Things’ opens worlds of possibilities, and marketers should not be held back,” Manion says.
Indeed, many enterprise companies doing a killer job with their apps have adopted Ensighten’s no-SDK approach to omnichannel marketing.
Gamers Drove Mobile App Development
So how did the Internet of Things evolve, and agile app marketing become a requirement for marketers? Mobile game developers drove the development, starting with mobile analytics to drive quality gaming experience. According to a recent Wall Street Journal report, Zynga really considers itself an analytics companies only “masquerading” as game company. Their key: Analytics drives game content around conversion over time. In other words, they are leveraging data to drive better in-app experiences.
The early use of these app tags was built into the game platforms along with analytic reporting tools – the platform or walled garden included content creation, data collection, analysis, and reporting in one place. It was a closed ecosystem that gave marketers what they needed to create better gaming experiences; but the ecosystem was still confined to the hard-coding process and all that waiting we talked about earlier.
Companies like Mixpannel, Segment.io, and Kissmetrics have built tools to optimize mobile apps, and are creating new metrics for mobile developers that are becoming as common as any page metric for the Web developer utilizing user location, trends, demographics, and a host of personal information as we have “opted in” to the device and it is co-located with all of us all of the time.
It is the use of this data, based on “the law of large numbers,” combined with personal information and aggregated across user cohorts – age, location, time of day – that yields powerful trending information giving developers keen insights into the use of their product in very short timeframes – hours and days instead of weeks and months. Metrics like Daily and Monthly Active Uniques (DAU and MAUs), and the tracking of user engagement within the app by cohort, now drive app developer decisions about features and the monetization of the application.
Yet, these new measures create new challenges, says Andrew Edwards, managing partner at Efectyv Digital. “If it were hard enough to compare page views to page views across one brand and another, it’s harder still to determine rough equivalents in mobile apps. Organizations will have to pay even closer attention to their KPIs in mobile, because there are no standards.”
But again, deploying technologies with the app in an agile fashion –with no hard coding, or waiting — remains a major obstacle. Yes, deploying analytics within an app is important. With a rigid SDK approach, all that analytics data must be pre-planned so it can be hard-coded in the first place. In contrast, Ensighten’s agile approach to mobile app marketing doesn’t require pre-planning and lets marketers change their mind, test new tools, and live the agile lifestyle they are used to.
Best of Breed Mobile Innovators
In the last quarter I’ve spent time meeting with and listening to some of the largest brands in the world talk about their direction and key use of mobile as part of their omnichannel strategy. What they have in common are the following:
- They are rapidly adding mobile applications
- They are becoming data-driven organizations and are creating data-driven processes
- They are transforming and re-aligning their organizations around data-driven, omnichannel demands by adding the right people and processes
- They are staying as open and agile as possible as the rate of change is monumental and unpredictable
Two of the brands I mentioned earlier – Getty and Vivint – are leveraging a no-SDK approach to mobile app marketing, and this has helped propel them as leaders in the space of omnichannel marketing. As mobile app game developers drove games, Vivint is driving the Internet of Things, starting with the connected home, and is a great example of thought leadership and mobile app vision. Vivint is serving hundreds of thousands of customers with smart technology for home security and management by bringing together omnichannel data to drive interconnected experiences. They have a three-fold mobile app strategy that to drives their vision of a connected smart home:
- Start slow and learn with a few basic mobile applications. Keep customer satisfaction high and measure with NPS and in-app metrics.
- Understand, test, and optimize customer engagement metrics within the app experiences and build KPIs with users and applications using analytics
- Broaden app offerings to other devices in the home, while keeping agile and responsive to customer needs. Let data drive their mobile app experience decisions.
Similarly, Getty is going “all in” with the agile, no SDK approach to mobile app marketing. Having moved from Web-based marketing, Getty is now creating a suite of mobile applications with a goal toward increasing customer engagement and loyalty. To do that, Getty is rapidly creating applications that engage their loyal visitors with their stunning images. More visits/time on the app means more customer loyalty for Getty. And more revenue. They are re-crafting their organization to support their mobile efforts.
In this new era of the Internet of Things, the age-old struggle for marketing agility presents fresh challenges. Understanding how to keep an agile pace, innovate on traditionally constrictive platforms, and deliver the right experience across a range of devices is key to today’s marketing mix.
Analytics is complicated and, because it is designed to provide facts, is not often as welcome in a marketing discussion as other disciplines. Analytics sits between marketing and technology in a place where neither party feels altogether comfortable; and too often the analytics consultant finds herself taking arrows from both sides.
Here are some suggestions for the analyst in order to make sure your analytics customers remain happy, whether internal or external.
1. Establish Legitimacy (Confidence)
Do you know what you are talking about? Make sure your customer knows it. Often the more insecure a stakeholder is, the more skeptical they are of you. When you meet these kind of folks for the first time, have handy a brief “elevator pitch” that lists your qualifications and experience in a friendly way. Then, avoid jargon and instead, talk in a way that some folks call “storytelling” but which I call “narrative.” This requires you know the data and where it points. In this scenario, you are the scout-leader heading the group on a hike to the facts.
Don’t make yourself a burden by going over the top, but when you wonder “should I check in,” it often means your subconscious has already answered that question and is trying to get your attention. Making sure your stakeholders are nearly as well-informed is key to keeping them happy. Communicate more judiciously than you would with a colleague. Your customer is more averse to surprises, and that’s mainly because they often have their own reports to do, and when you surprise them, then they have to surprise their boss. And their boss really does not like surprises. Keep from surprising your stakeholder, even when you think the news is good.
3. Test Before Launch
Have you heard of the “small technical glitch” that “caused a big problem”? It happens a lot more than you’d expect. Almost always, this is because no one has set up a proper testing environment; and tested whether the program creates unexpected changes in data collection or reporting. A test environment is a great way of avoiding surprises (see above). In many cases, it can spell the difference between a good analytics program and loss of confidence.
4. Pay Attention to Narrative
People are storytellers. Data doesn’t tell a story, but it does provide you with reports so that you can tell a story. Perhaps one day there will be a truly engaging storytelling robot but today, it is still the job of the human being to look at seemingly unconnected threads of information, see patterns, understand nuance and relative importance, and to create a story out of raw numbers. You’ll likely need data from different sources to create a fully dimensional picture for yourself, which then you will use to create the narrative that comprises the insight needed to make changes based on data. Without the narrative, it’s just machines talking to other machines.
5. Don’t Defend Technology at the Expense of Business Needs
Technology is not business, it is a subset and a provider to business. So when a non-technical person says why not, the technologist is ill-advised to simply say “we can’t do that,” assuming the non-techie will accept that “the technology just cannot do that.” First, you may not be right. Very often, there is a solution out there, and maybe you need to find it. Second, many businesspeople see a technology lack as your lack, because without technology, you would not be there at all. It’s OK to say the technology cannot do it if the technology cannot do it, but you will need to communicate that as a business concept. For instance, “there is no data source” is not nearly as effective as “we need someone to give us access to the data sources, do you know who that might be?” All of your reasons for doing things (or not doing things) must serve a business purpose, or you need to supply a plausible business reason why you can’t do it.
With these five weird tricks you should be able to lose weight, get cheap car insurance, and even keep your analytics customers happy!