Five Key Elements For A Big Analytics Driven Business Impact

There is, almost literally, an unlimited number of things you could focus on to create a high impact data-influenced organization.

And, as if unlimited is not enough, nearly every month your analytics vendors release new features, you discover new analytics solutions, and as your business is more successful (hurray!) there is a new mobile app to track or a new digital experience to problem-solve or a crazy online to offline campaign that upends everything unleashes a new layer of tactical activity.

In a world when your work will never be done, how do you assess that the core things necessary are present? How do you ensure that your can zig-zag with business strategy? What guarantees that agility and innovation are present in your analytics practice?

I believe there are five elements that have to be persistently present in the primordial soup at any company that expects amazing life to spring forth.

You’ll be surprised, there’s only one tool in that mix. It is not even an analytics tool. My reason for that is simple… At this point, it honestly does not matter which web analytics tool you use as long as it is a tool that is under active development by your vendor. Yes, some tools can dance on their left foot and others can only do so with their right foot. Not as important as you might think.

My recommended five elements are much more primal, their presence powers brilliant life to constantly evolve.

Here’s a little back story.

I was asked a few weeks back: “What companies should we proactively help with analytics, for free, so that they can make smarter data-influenced decisions?” I think the answer expected was my view related to the size of the company or their industries or those that might have Google Analytics or those with big advertising spends etc. My answer was: “Look for these two elements, if they are present then it is worth helping the company with free consulting and analysis. If they are not, no matter how much money or how many Analysts they have, helping them is a waste of time because nothing will live after your consulting is done .”

That lead to this post. At the end of this post, I’ll share the two that I recommended to the team in our conversation above. But first, let me do something a bit more expansive and share all the key elements necessary to ensuring an analytics existence that can help create a high-impact data-influenced organization.

Ready?

1. Google Tag Manager.

You live or die by the data you collect, and the quality of that data.

The single biggest limiting factor in your ability to think smart and move fast when it comes to taking advantage of the latest and greatest features is tagging your site.

I was speaking with a massive national insurance company recently. To implement a couple of my ideas they shared that it would take them seven months to update the code on their website. Obviously this is a multi-billion dollar corporation, some rigidity and layers are to be expected. But. Seven gosh darn months! I was not even asking for something crazy like moving from old lamer tags to a full featured Universal Analytics implementation. They already have bits of the latter, I’d recommended a couple simple upgrades.

No matter how good your idea, if you can’t get it implemented quickly, it is dead on arrival. Yes, you can blame the IT team and their long queue of things and prioritization process. DOA.

Data quality plays a role into this.

If you are a large company, ensuring QA adds more time and layers to the simple act of releasing code. And, most of the time, regardless of the size of the size of the company, you only know your code is not working post-launch when data is flowing in (not!). A good tag management platform fixes this problem as QA and other auditing is built into the tag manager’s deployment process.

Two reasons you need to ensure that you have Google Tag Manager.

Yes, yes, yes, there is also the benefit that you replace the massive glob of code on your site with one simple container tag. So beautiful. And, of course there is the delicious detail of this strategy not adding burden to your site’s visitor experience due to it’s async nature. Nice. Oh, and I agree that being able to pretty much one click deploy valuable things like exit-link, downloads, scroll tracking etc. is lovely. Ok, ok, ok it is a life saver to have 21 vendor templates for vendors as diverse as Adobe Analytics and Criteo and Firebase means you can use GTM for so much more than GA, and you can make functional calls for 61 additional ones including Marin, Nielsen, Twitter, Webtrekk etc. You got me, I am ignoring all the data layer and custom stuff!

All that is great. But, at the end of the day presence of a Tag Manager communicates to me that the company is serious about data collection and data quality. AND, that if I have good ideas, they will get to market very quickly – making our engagement worth the current interaction and the continued success of new ideas after the engagement.

If your company does not have a Tag Manager and its supporting processes deployed, you are simply not serious about wanting to make speedy smart business decisions. The size of your business is irrelevant. End of story.

Hence, if you don’t have one yet, get GTM as it is free and powerful. Or get any other dedicated tag management platform out there. Qubit is one. Our friends at Adobe have DTM . There are others, a google query away. Small recommendation… Just stay away from non-dedicated tools that do tag management and save the whales and are trying to help decide if skinny jeans are in or out.

2. Digital Marketing & Measurement Model.

If you don’t know where you are going, any road will take you there and you’ll be miserable when you arrive.

That’s it. That’s the problem afflicting the analytics ecosystem (regardless of if you have one part-time analyst and a tool or you have 50 analysts and 20 tools). We don’t know the strategic priorities of the business. We don’t know our line of sight is to those priorities. We don’t know what good and bad performance looks like. Problematic, right?

My solution to this, incredibly real and frustrating problem, is to insist on seeing the signed in blood version of the company’s Digital Marketing & Measurement Model.

It is the result of a simple, yet intense, five step process where each step requires the answer to a simple question – with active involvement of your Sr. Management.

Each step has a sweet name: Objectives | Goals | KPIs | Targets | Segments.

Simple, right?

Yet, I bet a big hug that the output of this simple five step process does not exist at your company. Heartbreakingly, you are driving a ship with no navigational instruments.

Everyone should have a well-defined DMMM.

Here’s mine for this humble little blog that makes no money except for charity from the sale of my books…

It should be easy to see how this brings massive focus to my analytical efforts. I know what is important, I know what to ignore, and I know what my boss (my beloved wife) really cares about. We are both aligned. I spend 50% less time on data analysis and 50% more time on driving actions that will impact those targets you see above.

You can easily see that now my dashboard is simpler. Any features I need to chase in my analytics tool (with GTM hopefully) are easier to identify. When I come to work I know what matters. And, most importantly, everything I’ll provide to my boss helps her deeply internalizes I am focused 100% on business priorities – rather than random big data sojourns.

That is what you want.

If you don’t have this, ideally signed in blood by your leadership team and you, then you are just messing around with data. If you are a government analytics team, you are wasting time. If you are a small business part-time analyst, you are wasting time. If you are an Analytics cog at a massive company like HP or Marks & Spencer, you are wasting time.

Don’t waste time, DMMM!

[Question four can be very hard to answer. Here’s a post that will help you figure out how to set targets by opening your mind about all the possibilities: Benchmarking Analytics Performance: The Options, Dos, Don’ts]

3. Macro & Micro Outcomes Content/Strategy.

This has nothing to do with analytics or analysts.

It simply helps identify the landscape of possibilities. Is your company walking the talk necessary for you in the analytics team to drive massive business success online and offline? .

Most companies solve for 2% of the possibilities. They only want to sell, if they are an e-commerce website. They only want to throw up a one page lead-gen form if they are B2B. They only want to ask you for a donation if they are a politician’s digital presence.

Solving for just an ecommerce order or just a lead or just a donation is ok in the sense that all these things drive short-term success for their individual businesses. Then challenge you have to recognize is each of these is only solving for 2% of the possibilities. The universe of possibilities from your digital presence is massive. Specifically, it is 98% more than your current single obsession.

Only having the content/strategy for 2% results in a very narrow focus for your analytics tools, processes and people.

Instead, every company should solve for a global maxima…. Yes, make the short-term money, it is necessary, but also do the but not sufficient part as well….

The above optimal strategy indicates that the company leadership is forward-thinking. It indicates a whole lot more people and empowerment when it comes to digital. It indicates imagination for a nonline impact. It indicates taking complete advantage of the See-Think-Do-Care clusters of digital intent.

In the above case, the implication on analytics team will be to give them a much wider canvas when it comes to creative analysis, people influence and business impact.

You might not realize how important this is. Let’s make it real.

Visit Kohls.com for just a few minutes, poke around, look for the macro and micro-outcomes. Very quickly, like me, you’ll be able to plot out this map…

Do you see what I mean when I say thinking strategically and solving for all clusters of digital intent? Online, offline, mobile, acquisition, brand love, relationships, product pimping, community stewardship and so much more!

Can you see how rich and impactful the role of the Kohl’s Analytics team will be with the above collection of both normal and innovative things? There is a lot to do, the business has bet big on digital. You are going to have access to a ton of data, a ton of customer behavior, that is driving a ton of business value. It makes Analytics a lot of fun and valuable. Analytics can actually have a meaningful long-time impact.

Now try Belk. Or even Macy’s who’s not really a competitor of Kohl’s. In both cases, a lot narrower business focus, a lot smaller expectations of digital, and hence a lot less influence or impact Analytics can bring. Not that either of these two massive companies don’t have 500 Digital Analysts each. They do. But, the creativity, expansiveness, and long-term true driving force won’t quite be there.

You want the global maxima. If you don’t have it, you are certainly doing Analytics and having some impact on short-term narrow success. But, you will never be as influential as you deserve to be. Fix it.

4. Analytics Resources Focus: 15 | 35 | 50

My fav.

You’ve heard me constantly stress the value of the 10/90 rule for investment in Analytics. For every $100 available for investment in Analytics… Invest $10 in tools and implementation, invest $90 in big brains to analyze the data.

The rule was published a decade ago (#omg), and has only become more true with every passing data with the explosion of free amazing tools combined with the crazy-increase in complexity in data and customer behavior.

Even if you follow the 10/90 rule, it is important to focus our time and resources optimally. I suspect this is true in your company, a whole lot of your effort is spent on fixing/upgrading implementations (see #1 above) and creating CDPs (customized data pukes). This is sub-optimal.

There is a difference between reporting and analysis.

If you have the latter, your Analysts will… actually be Analysts. They will be forced to develop a strong understanding of all facets of the business, they will be empowered to apply their complete quiver of skills to deliver specific insights that include actions, and they will identify the business impact for each action. [The extremely sexy I-A-BI.]

Not every company can afford to have full-time people dedicated to analysis. Hence let me make optimal recommendations for three types of companies.

If you are a handful of employees small business, here’s your balance between data capture, data reporting, data analysis: 05 | 20 | 25.

You are a small business, I get it, you only have a part of one resource dedicated to data. No problem. Ensure that part-time resource is spending their time with a 05 | 20 | 25 balance. At your size, you’ll still win with data.

If you are a numerous employees medium sized business that is growing at a ferocious pace, here’s your DC | DR | DA balance: 10 | 25 | 65.

Don’t ask for too much reporting. If you were going to have three “Analysts”, pay more and hire two real Analysts. Then, ask for real analysis. Ask for I-A-BI.

No organization will be born into this 10 | 25 | 65 distribution on day one. It will take evolution to get there. Here are the time based milestones (on your left) that I set for my medium sized clients who are growing at a ferocious pace…

The reason for this rapid evolution is in the right-most column.

Until we get to a 65% standard for DA, we are not adding value that is justified by the $$$ and people investment in Analytics.

Lastly, if you are a massive employees corporation here’s your DC | DR | DA balance: 15 | 35 | 50.

It is ironic that you have a team of 25 Analysts, 5 more people dedicated to implementation, a huge analytics tool support contract with consulting agency resources attached to it, and the best you can do is 50% Analysis. It is a very sad reality.

Massive companies think reporting (#cdpsFTW!) is the solution to all known problems. It is silly of course. But, I’ve discovered that I need to be humble. Massive companies cannot function without the soothing balm of data puking. Hence, I try to salvage 50% time and resources to be dedicated to pure, sweet, amazing Analysis. That will ensure a decent return on your analytics investment .

So. What do you have?

If you don’t have at least 50% of the analytics time and resources dedicated to solving for the known unknowns and the unknown unknowns, your analytics practice is set up for nothing serious. Cutting it by 75% will have marginal impact on your business success. If you don’t believe me, give 75% of your Analytics team a one week vacation and you’ll notice hardly anyone else in your massive corporation will notice.

DA. 50% minimum.

[Bonus: Nine skills that Analysis Ninjas have to possess!]

5. Optimal Analytics Team Org Structure.

How do you empower the above?

Ensure that the Analytics team is structured so that the above focus balance, and other things in this post, can happen. Don’t do this and no matter how much your investment in analytics resources and tools, sucking will ensue.

If you’ve read Web Analytics 2.0 you might have a sense for my point of view on this topic already.

When organizations are small or medium sized or extremely new, a centralized model typically works the best.

You are starved for resources, the business is simple enough and everyone knows everyone, you are better off with your finite Analytics resources in one place, executing off one data playbook and one clean Digital Marketing and Measurement Model.

The entire team/person reports to a CxO, bringing with it influence. The functions, or divisions, will rely on the central team for all data (analysis!) needs, and will provide input in terms of their strategy and needs (they’ll actually tell you their wants, but your personal smarts will be proven when you can get them to needs). Just ensure that you note the balance of DC | DR | DA recommend above, and you’ll do fine.

When organizations are growing from medium to big at a rapid clip, a decentralized model has a high impact.

The business is changing really fast, the disparate parts of the org are evolving at different speeds and they also tend to have distinct needs. Having your analytical resources directly in the business function/divisions brings speed and ability that is critical for business success. Yes, there will be some inefficiency but never let a dogma get in the way of doing the right thing.

It is ideal, even in a decentralized model, that you have one person, perhaps in your IT org, officially own implementation (or even better, all facets of DC). This will help bring some sense of sanity, and reduce at least some inefficiency.

For established businesses with a lot at stake (and analytics resources) in my experience the optimal model is centralized decentralization.

A small experienced core group at the center, lead by the Analytics Czar (yes!) with responsibility for every facet of the entire company’s data collection, data reporting and data analysis. Additionally, an Analyst, or more, embedded directly in each business function – who dotted lines into the central org.

The center owns the overall analytics strategy, decision making around standards and tools and processes for the company, the evaluation of new analytics solutions, investment in training, and all the complex experimentation required when new approaches to tools, data, analytical techniques appear on the horizon. They are also responsible for the overall analysis to fill corporate needs for cross-functional analysis. The purpose of this centralized analysis is to keep everyone focused on what’s good for the overall business, and to help fuel strategic business decisions.

Analysts that sit in the business function are responsible for 100% of functional analysis needs. They do the little reporting that is required, lots of delivering of IABI, ensuring alignment of data needs with functional priorities, local training, and bubbling up functional needs up to the central team.

Centralized decentralization provides a model solve for both operational efficiency and strategic influence/impact.

So, what do you have? For your size, is your org set-up with the optimal model? Or, is your org just floundering about with analytics in some dark basement or in random corners? Identify, take action.

[Bonus: Surely you are also wondering, who should the Analytics function report to? I have an answer for you, but here’s something better… a framework for critical thinking on analytics ownership.]

There they are. Five elements that signal existence of the primordial soup capable of birthing, and evolving, the kind of analytics existence that every company deserves.

If these five elements don’t exist, it is important to realize that that will not only cause data efforts to have a negative return on analytics, it will also have a negative impact on your career. Simply because if you are not doing great work that delivers huge data-influenced business impact, your career is not going anywhere.

Ideally that is not the case, but if it is then you now have a very specific prescription of how to fix the gaps in your company’s strategy. They win, your career takes off like a rocket!

Oh, and to close out the back story from the top of the post. I’d mentioned I recommended two elements the team should look for in the clients before giving them free consulting and resources. It was #1 and #3. It would be hard for an outsider to assess other things, but these two they could and it would tell them all they needed to know. Since I am very fond of my audience here, and most of you work inside companies, I wanted to give you all five. 🙂

As always, it is your turn now.

Is there an element that has to exist that I did not cover? Why do you think it is particularly critical? Do all five of the elements above exist in your company? If not, which one’s missing in your company or others you’ve worked at? Would you share tips that have driven the success of any one of the five elements, of all of them? I’m particularly curious which organization model you think drives most true IABI-centric data analysis?

Please share your feedback, learnings, critique, via comments.

 This article was originally published at Kaushik.com, by author Avinash Kaushik.

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