Data driven marketing

Rational decisions based on facts and statistics are more likely to get you the business results you want – such as growth, profits and customer loyalty. That’s the promise implied by “data driven-marketing”. And it’s a whole lot better than intuitive decisions based on qualitative information, ‘gut feel’ or experience. Or is it?

 

Forrester reports that, in the USA, the companies that are best at data science …
• are twice as likely to be leaders of their segment
• have significantly higher revenue growth and profits
• and: are most likely to be in the size group 1.000 to 5.000 employees.

Now its tempting to read this an endorsement for data science. Also, to infer that data science is potentially the ‘Secret Sauce’ for improving business results among the Hidden Champions of the Mittelstand. There are two big ‘buts’.

First: Correlation is not the same as causation. Second, we need more information to determine what is cause and what is effect. Did those companies in fact become the best at Data Science because they spent twice as much budget on it than the others?

The goal of data driven marketing is to create clarity and enable action. Revealing patterns, trends, and associations, especially relating to human behaviour and interactions sounds like a great idea. But we can’t take data ownership for granted.

Sunand Menon notes that “many organisations assume that if they collect the data and house it in their systems, it must be their data”. But any processing of personal data in the EU immediately falls within the scope of GDPR. So it pays to evaluate data ownership early on, and if in any doubt at all, to get legal advice from a lawyer or Data Privacy expert before starting.

 

“It’s critical to treat customers and their data with respect.”
JOHN FORESE

 

Where to begin?

How can marketers get started with data-driven marketing? Brad Brown suggests that managers first ask themselves “Where could data analytics deliver quantum leaps in business performance?” The next steps are to define a strategy for data analytics and implement it. While this may be a valid approach for large enterprises, it does not seem appropriate for the Mittelstand. This scenario describes a technique in search of a raison d’etre. In mid-size organisations its called “putting the cart before the horse”.

In a brief “how-to” article, Thomas Redman advocates these steps to data analysis:
• formulate a question and write it down
• collect the data
• draw pictures to understand the data
• ask the “so what?” question

The “so what?” evaluation tells us whether the result is interesting or important. Many analyses end at this point, says Redman, because there is no value beyond the “so what?”.
Given that Mittelstand Marketers can afford to waste neither time nor resources, it makes more sense to ask the “so what?” question before we even start collecting the data. That way we can focus on the questions that will deliver result that are both interesting and important.

 

Common data standards

Before we can analyse the data, we have to get hold of it. Data mining – identifying and using the data you already have – is a sensible place to start. And yet this is where the difficulties begin.

Companies often hold data in multiple systems. Systems that were originally designed to serve distinct business units, departments or organizational functions. These systems were often built without reference to each other. As a result, they frequently use inconsistent data definitions and structures – even for the simplest of attributes.

The international standard ISO-3166 for example, defines several ways to describe countries: Alpha-2, Alpha-3, UN M49, Name. (thus: Germany, DE, DEU, 276). To marry up systems that use different definitions, the structure must first be recognised and then translated into a common format before the data can be combined for analysis. In very old systems however, the programmers may not have used ISO codes for standard dimensions and characteristics, which creates further complications and additional work.

 

69% of organisations ARE UNABLE TO PROVIDE A COMPREHENSIVE, SINGLE CUSTOMER VIEW

 

Silos make it hard to manage and analyse enterprise-wide data. This example is just the tip of the iceberg. Suffice to say, integrating data from a variety of silos is slow and resource intensive. Companies that have grown by acquisition will know this situation only too well. Rather than try to integrate two completely different systems, the usual decision is to keep one and close down the other.

There’s another factor at work here. The reality is that old systems reflect old business practices. And this has two important implications, both of which are uncomfortable. On the one hand, the way data is stored and processed today is not necessarily relevant for deciding the processes you need today or tomorrow. The reverse is also possible: there may well be types or categories of data that you need for an analysis, that simply aren’t available in the way you want it, because it has never been collected in that manner.

An example of this is the software vendor who sold a bundle of products using a single contract with a single price on the invoice and a single line entry in the CRM. It proved impossible to analyse accurately the market penetration, revenue attribution or competitive situation for each of the individual software products in the bundle. The information could not even be estimated by analysing customer technical support enquiries. The lack of information and insights at the desired level of detail hindered budget allocation, investment in product development and planning of marketing activities.

 

72% of companies say that managing multiple CRM systems across geographies/ technology silos is challenging

 

Next up: you may want or need to combine data from two different systems – only to find that they have been designed on a completely different view of the way the world works.

A classic example is the B2B Marketer who wants a “single view of the customer”. To achieve this, data from the CRM should be combined with data from the Online Marketing system. This is easier said than done. In the B2B world, CRM systems are designed around the fundamental unit of a customer organisation – each of which may have multiple contact persons. By contrast, the basic unit in an Online Marketing system is a contact person – and that contact person record can exist without belonging to a business.

Mapping contacts from the two systems against each other causes headaches whichever direction you try to solve it. The headache is that some data simply cannot be integrated – and is therefore unusable for analysis. Any loss of data in the analysis means a loss of accuracy in the evaluation.

“Rubbish in, rubbish out” has been a mantra of computing since the earliest days; the need for data quality acknowledged and understood. But once again reality gets in the way of the two key characteristics of data quality. Data completeness means that if you decide to add a characteristic to a database, you must add this piece of information throughout the entire database. Similarly, if you’re going to collect data, it has to be accurate – both at the time of collection and later on at the time of analysis.

As we know, the world is in a constant state of flux. Dun and Bradstreet – an organisation that provides credit rating services for business – invests huge amounts of effort in keeping its records of millions of global organisations up to date. The company knows only too well how fast the world is changing.

 

Each minute of an eight-hour working day:
• 211 business will move
• 429 business telephone numbers change
• 284 executive or business owners will change.
Dun & Bradstreet

 

Faced with large volumes of data and the rapid velocity of change, it’s clear that a one-time data analysis project is going to have a very limited half-life for supporting decision-making.

 

Accessibility in real time

When data analysis is repeated on a regular basis, data quality becomes even more important. The data collection, cleansing and integration steps have to be repeated efficiently and effectively.

To improve data quality Thomas Redman advocates establishing a process management cycle. The first step is to measure data quality; the second, to decide which approach to use to improve quality. Redman lists and comments on three choices:
1. Unmanaged – not recommended.
2. Find and fix – resource intensive.
3. Prevent errors at the source – the best option.

 

“Improving data quality requires a cultural shift within the organization.”
THOMAS C. REDMAN

 

Preventing errors at the source implies a change in practice. Instead of analysis being implemented as an activity (whether manual or batch), it has to be re-designed to become an ongoing process. A more advanced line of thought is to re-design and implement business processes so that they automatically generate the data that is needed for analysis. Data as by-product of daily business, in fact. This transition from activity to process is a central and recurring aspect of digital transformation in marketing.

 

Data analysis

Data analysis describes the process of inspecting, cleaning, transforming, and modelling data to gain insights that support decision-making.

By establishing baselines, Marketers can identify patterns such as (say) seasonality. By distinguishing between noise and signal, medium-term trends can be accurately identified, enabling marketers to re-allocate resources more quickly in response to evolving markets.

Scott Neslin advocates analysing the sales recency curve to investigate whether or not to invest marketing budget in a customer. He acknowledges that the results can be ambiguous. The sensible approach, he says it to derive an action plan from the data, test it and measure the results.

 

“A lot of stories emerge from customer data.
The trick is figuring out which story to listen to.”
SCOTT A. NESLIN

 

For Harald Fanderl, the greatest value of analysis comes from “pinpointing cause and effect and making predictions”. To improve customer journeys, Fanderl examines just the top three to five that contribute most to customers and the bottom line. “Narrow the focus to cut through the data clutter and prioritize,” he says.

What about sales managers – which metrics should they track? Scott Edinger prefers to measure the process rather than the outcome because managers have control over the process; whereas the outcome is determined by another variable which cannot be controlled (the customer).

 

“Managing the things you can control, will give you the best chance for success.”
SCOTT EDINGER

 

The simplest analytics questions can be enormously powerful. Michael Schrage uses the Pareto principle to ask which 20% of customers generate 80% of the profits. And then he iterates this approach to identify the most profitable segments for future action.

 

“Learn which customers are profitable and which ones aren’t.
It makes it easier to see the opportunities.”
CHRIS BRIGGS

 

The goal, says Chris Briggs, is to: “make informed decisions and not let the numbers lead you astray.” Though this is far from easy. As Andrew O’Connell and Walter Frick observe, the numbers don’t lie but: “can be slippery, cryptic, and, at times, two-faced. Whether they represent findings about your customers, products, or employees, they can be maddeningly open to interpretation.”

 

Advanced analytics

A good analysis provides a marketer with reference data that have predictive power. These insights are more than just a one-off event; they are patterns that describe baselines, trends, relationships.

There are several types of pattern that regularly appear in both the natural and man-made world: standard distributions; time series; 80:20 Pareto relationships; power curves with longtail distributions; direct and indirect causal relationships. Each of these describe a different context for analysis.

The approach to finding these patterns will depend in part on the available resources. If huge amounts of data, of high quality (completeness and accuracy) are readily available from a small number of systems and require little effort to integrate, then machine learning may be a good option. Machine-learning software identifies patterns in data and uses them to make predictions. So the work sequence is to let the machines identify the patterns in the data; and then test the patterns for their predictive power.

But who exactly is going to do the analysis? And what do you do if your organisation doesn’t have the skills or tools in-house? “Small and medium-size businesses are often intimidated by the cost and complexity of handling large amounts of digital information,” says Phil Simon His solution: hire external data scientists via websites such as Kaggle [www.kaggle.com].

 

“Kaggle lets you easily put data scientists to work for you,
and renting is much less expensive than buying them.”
PHIL SIMON

 

If on the other hand, the data is lacking in volume or quality, or if integration from disparate systems requires a lot of time and effort, then the best approach may be to begin by narrowing the scope of the project. Marketers do this by focussing on a clearly defined hypothesis before defining what data is necessary and which analysis will prove or disprove the hypothesis.

 

“In a world that’s flooded with data, there’s too much of it to make sense of.
You have to come to the data with an insight or hypothesis to test.”
JUDY BAYER AND MARIE TAILLARD

 

At the very root of data driven marketing is the ability to ask powerful questions. Asking questions is a skill. It is possible to develop it and get better at it, with practice, over time.

One approach is to reverse-engineer the issue and identify the really powerful questions by starting with a clearly defined goal in mind:
• What decision do you want to make?
• What insights will enable that decision?
• What questions will generate those insights?
• What data do you need to answer those questions?

Managers who have internalised their knowledge of a subject and their experience of a field, know –seemingly intuitively – which questions need to be answered and whether it’s worth investing effort in rigorous data-driven analysis. Perhaps this is why Page 7 of the Forester report states: “48% of companies use intuition over data to guide their decisions”.

 

Human vs machine

So who makes the better decisions – the human or the machine? Andrews McAfee is one of several writes who has researched this area. In his view, “data-dominated firms are going to take market share, customers, and profits away from those who are still relying too heavily on their human experts”.

“When experts apply their judgment to the output of a data-driven algorithm, they generally do worse than the algorithm alone would,” he reports. “Things get a lot better when we flip this sequence around and have the expert provide input to the model.”

MAfee quotes from Ian Ayres book Super Crunchers: “Instead of having the statistics as a servant to expert choice, the expert becomes a servant of the statistical machine.” In other words, the expert’s job is to ensure that the process is: “quality data in, quality insights out”.

 

“The single biggest challenge any organization faces in a world awash in data is the time it takes to make a decision.”
TOM DAVENPORT

 

Which brings us to the issue of what we actually do with the results. It may be a good idea to listen to Tom Davenport ‘s comment on decisions. In the final analysis, there’s not much point in investing time and effort in data-driven marketing, if your management team can’t or won’t act promptly on the insights.

Nine reasons why People are more important than Technology

The Digitalisation of B2B Marketing appears to have run out of steam among German mid-size manufacturing organisations (the ‘Mittelstand’).* It may sounds like a regional issue, but it’s not; in fact, it’s symptomatic of a global trend. “Why?” is an interesting question. But it’s more useful to ask, “What to do about it?”.

Executive Summary:
CMOs will make faster progress and get more value from the Digitalisation of
B2B Marketing if they switch their focus from technology to people.

Why? – Issues with Technology

1. We have enough technology already.

The anecdotal evidence suggests that most B2B organisations already have all the technology they need: a website with content management system plus analytics, search tools to optimise organic findability, advertising or Social media sites to maximise reach, email systems for nurturing and outbound communication, perhaps even a CRM that hooks into the ERP system. They often have a whole lot more besides.

The research report on Marketing among mid-size firms by Saxoprint indicates that online marketing is taking a growing share of the marketing budget: currently 24%, and set to increase to 33% in two years’ time.

2. We don’t need more technology

The proportion of businesses that have outgrown their current system and genuinely require additional functionality, is very small. The reality is that – for most organisations – the functionality of current online marketing systems is not fully exploited. In fact, having too many systems can actually cause problems.

“Data silos and a lack of data quality often stand in the way of a better digital customer relationship,” reported ComputerWoche in April 2017. Common complaints are that data is siloed, duplicated, low quality, redundant or can’t be integrated into information flows across departments.

3. Changing technology incurs heavy costs.

Switching to a new system involves duplication and transition. First the costs of running two systems in parallel for two, if not three quarters, before the old system can be turned off. Then additionally, there are change management issues: the direct costs of re-training users in different systems; and the loss of efficiency while defining new business processes and learning new working habits. This is not a route to be taken lightly.

Implications for People

4. Digital Unicorns and other mythical creatures

There has been increasing demand in 2017 for Digital Marketers who have expert user status in multiple online toolsets. For example, job adverts looking for: “SEO plus PPC plus CMS plus eMail plus creating Content for Blogs and Social sites”. The reality is that each of these areas is a specialisation in its own right. So is this a sensible expectation? Well, that’s a completely different matter.

The German language has an expression that really sums it up well: an “eierlegendes Wollmilchschwein” is an animal that lays eggs, gives wool, produces milk and makes a brilliant rasher of bacon, too. It is of course, entirely imaginary. The English expression ‘Jack of all trades, Master of none’ also springs to mind.

A global issue

So far, I’ve been talking about the German mid-size market. But a 2017 report published by CEB (a global research-based organisation, now owned by Gartner) indicates that this pattern is far more widespread.

In its report “2017–18 Marketing Talent Trends” CEB notes a “robust increase” in investment on systems and technology over the past three years (currently between 7% and 10% of the entire budget). The bulk of this, says CEB, is going toward digital investments, “particularly those around personalization”.
But is all that investment doing any good? Says CEB: “our 2017 data shows little to no improvement over the past three years in marketers’ core understanding of the digital landscape”.

CEB asked marketers to rate six barriers to organizational marketing excellence and came to this conclusion:

“The barrier cited least was the availability of marketing tools. In essence, marketers are telling us they don’t need more data, more machines, or more systems; they need to be upskilled to use the tools they already have.”

It’s not easy to brush this off: CEB information is based on research of its membership and its presence in all major economies. The company cites a global survey size of 93.000 respondents for this information.

Opportunities for your Business

The willingness to consider buying new technology or the ability to get new headcount approved indicates that the pain which Marketing Directors feel is very real. But, as we have seen, those standard responses – new software and / or additional staff – are unlikely to resolve the issues.

So how do you, as CMO of a mid-sized manufacturer, successfully make progress with the Digitalisation of Marketing?

5. Make more effective use of the technology you already have

This is certainly a sensible place to start. The key questions to ask – in sequence of sophistication – are:

  • how to get the most out of individual technologies?
  • how to combine them to fulfil specific tasks more effectively?
  • how to re-design tasks to address business objectives more effectively?

These approaches begin to shift the focus from having technology to how we use it, but to make them work, they have to be more tangible …

Perhaps it’s time to get more specific and advocate two more paths:

6. Boost the budget to external agencies.

In highly specialised and skilled areas like SEO and Online advertising, inexperienced staff can actually damage your company ranking, rating, score, etc. in the short term and end up costing you more long-term.

That being so, increasing budgets to agencies is not only the fastest, but also the safest way to ramp up volume or to increase marketing reach in a short amount of time. The upcoming EU Privacy law is another area where risk-taking is a false economy. A small budget for external advice can save the Marketing department both time and reputation – and potentially a lot of money as well.

7. Invest in the productive value of your current marketing staff.

Product-based user training will help staff become more efficient with a toolset, but it will only take you so far in effectiveness. What businesses really need is hands-on knowledge transfer from highly experienced outsiders who work alongside your staff on-site. By working together on specific projects, your staff can acquire best practices, learned at top companies, quickly and effectively.

The Online Marketing processes that I designed for one client during a ten-day knowledge transfer project, for example, were used consistently every month for six years. This type of engagement often represents a highly effective investment.

What we need to do, to increase Marketing effectiveness

To raise Digitalisation of Marketing to the next level of effectiveness, I believe CMOs need to shift their attention from technology to people.

More specifically, in mid-sized organisations – which don’t have huge Marketing teams – it becomes vitally important to decide two key issues:

  • which technologies are mission critical and which aren’t;
  • which skillsets should stay in-house and which can be sub-contracted out.

What I’m about to suggest may sound counter-intuitive:

8. The implementation of mission-critical technologies should be sub-contracted out.

We should not be entrusting hands-on responsibility for four mission-critical technologies to the new junior employee, Jack (or Jill) Alltrades, in the naïve hope that they can deliver A-grade results across all four areas with a maximum resource of 25% FTE each.

Instead, we should be partnering with specialist agencies, each dedicated to a single field. This approach ensures that volume can be ramped up on demand. It also ensures that the activity continues throughout the year, uninterrupted by professional training, vacations or sickness.

Technology-based skillsets are just one enabler of digitalisation. In practice, additional knowledge is required.

9. The skillsets that must stay in-house are the ones we haven’t built up yet.

The CEB report asked Marketers to rank six individual barriers (as opposed to corporate barriers) to marketing excellence. The top two issues are “better define individual roles and responsibilities” and “increase collaboration among teams”. Once again CEB is citing a sample size of 93.000 respondents for this information.

So the job that needs to be done, is one that CMOs can start now: people empowerment. In my view, this means a gradual internal transition from “doing activities” to “owning processes”.

My own experience is that people relish the challenge of owning processes, taking responsibility for designing workflows, discussing and agreeing meaningful KPIs with other teams or departments, interpreting reports (created by specialist agencies), gaining insights that can be used to make iterative improvements to processes … In short, creating a closed feedback loop for a learning organisation.

Who are the best candidates for these roles? IMO, not the staff of external agencies. (This is not a reflection on their competence, but simply a corporate preference.) This is where established and trusted in-house staff members will find space to create their new roles in the digital economy. Their value to the company lies in their experience of products, customers and processes and that experience is an asset that must be nurtured and retained. At the same time, company confidential information must stay in-house. A transition from “doing activities” to “owning processes” ensures that both goals are met.

Do you agree?
Comments and feedback are welcome …

* Der “Digitalisierungsindex” in deutschen mittelständischen Unternehmen wird von TechConsult im Auftrag der Deutschen Telekomm recherchiert. Die Ergebnisse für das produzierende Gewerbe zeigen für die Bereiche “Kundenbeziehungen” und “Digitale Angebote und Geschäftsmodelle” keine Veränderung von 2016 bis 2017. Demgegenüber verbesserte sich der Bereich “Unternehmensproduktivität” gegenüber 2016 um 3 Punkte auf 58/100 im Jahr 2017, während der Index für “IT-Sicherheit und Datenschutz” um 2 Punkte auf 68/100 zulegte.

Three Good Reason Why …

On 10 October I’ll be leading a Workshop at the UX-Day Conference in Mannheim, Germany.

The title is “How I learned to love the GDPR” (the upcoming European Privacy law which will impact all marketing by international organisations into the EU).

We’ll kick off with a swift overview of the impact of this law for B2B Marketers, which then leads into an exploration of six functional areas where Marketers can really benefit from professional UX-Design.

After that we’ll open it up for discussion and best practice sharing.

And the three reasons to love GDPR?

  1. All digital projects that collect and process customer data are urgent, because they have to be compliant by May 2018.
  2. The penalties for ignoring the laws are so high that these projects get the attention of the Top Management team.
  3. Which also means that reasonable resource and budget requests will be a whole lot more affordable than the penalties for non-compliance.

PS – The workshop will be in German … but let me know if you want a version of the presentation in English.

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