Tag Archives: customer insight

A New Framework for Your Analysts

As we focus on Analytics & Data Science, I’ve been reminded of how a Competency Framework can help.

Both work with clients, and my own experience in creating and leading analytics teams has taught me that such a tool can help in a number of ways. In this post I’ll explain what I mean by a competency framework and the different ways it can help analytics, data science or customer insight leaders.

I wonder if you’ve used such a tool in the past?

Across generalist roles and larger departments, the use of competencies has become the norm for many years, as HR professionals will attest. However, sometimes these definitions and descriptions feel to generic to be helpful to those leading more specialist of technical teams.

But, before I get into overcoming that limitation, let me be clear on definitions.

A dictionary definition of competency explains it as “the ability to do something successfully or efficiently”. In practice, in business, this usually means the identification of a combination of learnt skills (or sometimes aptitude) & knowledge that evidence that someone has that ability (normally to do elements of their job successfully). HR leaders have valued the ability for these to be separated from experience in a particular role, thus enabling transferable competencies to be identified (i.e. spotting an individual who could succeed at a quite different role).

Defining a competency framework

Building on this idea of competencies as building blocks, of the abilities needed to succeed in a role, comes the use of the term ‘competency framework’.

The often useful, MindTools site, defines a competency framework as:

A competency framework defines the knowledge, skills, and attributes needed for people within an organisation. Each individual role will have its own set of competencies needed to perform the job effectively. To develop this framework, you need to have an in-depth understanding of the roles within your business.

Given many Analytics leaders have come ‘up through the ranks’ of analyst roles, or are still designing & growing their functions, most have such an in-depth understanding of the roles within their teams. Perhaps because HR departments are keen to benefit from the efficiencies of standardised competencies across a large business, there appears to have been less work done on defining bespoke competencies for analytics teams.

See also: The Challenges of ‘Data Wrangling’  

Having done just that, both as a leader within a FTSE 100 bank and for clients of Laughlin Consultancy, I want to share what a missed opportunity this is. A competency framework designed to capture the diversity of competencies needed within Analytics teams has several benefits as we will come onto later. It also helps clarify the complexity of such breadth, as we touched upon for Data Science teams in an earlier post.

The contents of an Analytics competency framework

Different leaders will create different flavours of competency framework, depending on their emphasis & how they articulate different needs. However, those I have compared share more in common than divides them. So, in this section, I will share elements of the competency framework developed by Laughlin Consultancy to help our clients. Hopefully that usefully translates to your situation.

First, the structure of such a framework is normally a table. Often the columns represent different levels of maturity for each competency. For example, our columns include these levels of competency:

  • None (no evidence of such a competency, or never tried)
  • Basic (the level expected of a novice, e.g. graduate recruited to junior role)
  • Developing (improving in this competency, making progress from basic ‘up the learning curve’)
  • Advanced (reached a sufficient competency to be able to achieve all that is currently needed)
  • Mastery (recognized as an expert in this competency, or ‘what good looks like’ & able to teach others)

Your maturity levels of ratings for each competency may differ, but most settle for a 5 point scale from none to expert.

Second, the rows of such a table identify the different competencies needed for a particular role, team or business. For our purposes, I will focus on the competencies identified within an Analytics team. Here again, language may vary, but the competency framework we use at Laughlin Consultancy identifies the need for the following broad competencies:

  • Data Manipulation (including competencies for coding skills, ETL, data quality management, metadata knowledge & data project)
  • Analytics (including competencies for Exploratory Data Analysis, descriptive, behavioural, predictive analytics & other statistics)
  • Consultancy (including competencies for Presentation, Data Visualization, Storytelling, Stakeholder Management, influence & action)
  • Customer-Focus (including competencies for customer immersion, domain knowledge (past insights), engagement with needs)
  • Risk-Focus (including competencies for data protection, industry regulation, GDPR, operational risk management)
  • Commercial-Focus (including competencies for market insights, profit levers, financial performance, business strategy & SWOT)
  • Applications (including competencies for strategy, CX, insight generation, proposition development, comms testing, marketing ROI)

Variations on those are needed for Data Science teams, Customer Insight teams & the different roles required by different organisational contexts. Additional technical (including research) skills competencies may need to be included. However, many are broadly similar and we find it helpful to draw upon a resource of common ‘holistic customer insight’ competencies to populate whichever framework is required.

If all that sounds very subjective, it is. However, more rigour can be brought to the process by the tool you use to assess individuals or roles against that table of possible scores for each competency. We find it helpful to deploy two tools to help with this process. The first is a questionnaire that can be completed by individuals and other stakeholders (esp. their line manager). By answering each question, that spreadsheet generates a score against each competency (based on our experience across multiple teams).

Another useful tool, especially for organizations new to this process, can be for an experience professional to conduct a combination of stakeholder interviews and review of current outputs. Laughlin Consultancy has conducted such consultancy work for a number of large organizations & it almost always reveals ‘blindspots’ as to apparent competencies or gaps that leaders may have missed.

However you design your scoring method, your goal should be a competency framework table & consistent audible scoring process. So, finally, let us turn to why you would bother. What are some of the benefits of developing such a tool?

Benefit 1: Assessing individual analysts’ performance

All managers learnt that there is no perfect performance management system. Most are, as Marshall Goldsmith once described them, stuff you have to put up with. However, within the subjectivity & bureaucracy that can surround such a process, it can really help both an analyst & their line manager to have a consistent tool to use to assess & track their development.

I have found a competency framework can help in two ways during ongoing management & development of analysts:

  • Periodic (at least once a year) joint scoring of each analyst against the whole competency framework, followed by a discussion about different perspectives and where they want to improve. In this process remember also the greater benefit of playing to strengths rather than mainly focussing on weaknesses.
  • Tracking of development progress and impact of L&D interventions. After agreeing on priorities to focus on for personal development (and maybe training courses), an agreed competency framework provides a way of both having clearer learning goals & tracking benefits (did competency improve afterwards).

Benefit 2: Designing roles and career paths

Analytics & Data Science leaders are largely agreed that a mix of complementary roles are needed to achieve effective teams. However, it can be challenging to be clear, when communicating with your teams & sponsors, how these roles both differ & work together.

Here again a consistent competency framework can help. Scoring each role against the competency maturities needed, can enable a manager to see how whole team scores or any gaps still left. It can also help in more objectively assessing candidates suitability for different roles within a team (e.g. are they stronger at competencies for ‘back office’ modeller or ‘front of house’ consultant type roles).

See also: Insurtech: How to Keep Insurance Relevant  

If that benefit provides more consistency when considering peer-level opportunities, this tool can also help guide promotion opportunities. It can help you define the different competency maturities needed, for example, by junior analyst verses analyst verses senior analyst verses analytics manager. Such clarity enables more transparent conversations between analysts & their managers (especially when one can compare & contrast an individuals competency score with those needed by different roles).

Seeing how those competency profiles compare at different levels of seniority for different technical roles, can also enable a manager to see options for career development. That is, there are often options for junior members of the team (rather than a simple climb up the functional ‘greasy pole’). Examples might be: development of statistical skills to pursue a career path in the modelling roles; development of data manipulation skills to pursue a career path towards Data Engineer; development of questioning & presentation skills to aim for a business partner role, etc.

Benefit 3: Identifying your team L&D priorities and where to invest

Used together, all the elements mentioned above, can help an Analytics leader identify where the greatest development needs lie (both in terms of severity of gap & number of people impacted).

Comparing the competency profiles for roles needed in team, with current capabilities of role holders, can identify common gaps. Sometimes it is worth investing in those most common gaps (for sufficient numbers, it’s still worth considering external training).

Then you can also compare the potential career paths & potential for development that managers have identified from conversations. Are there competency gaps that are more important because they help move key individuals into being ready for new roles & thus expand the capability or maturity of overall team?

Much of this will be subjective, because we are talking about human beings. But having a common language, through the competency framework tool, can help leaders better understand & compare what they need to consider.

Do you use an Analytics Competency framework?

If you are an Analytics or Data Science or Customer Insight leader, do you currently use a competency framework? Have you seen how it can help you better understand the capabilities of individuals, requirements of roles & how both best fit together in an effective team?

Do you have the means to have meaningful career path conversations with your analysts? Being able to do so can be key to improving your analyst retention, satisfaction & engagement with your business.

I’m sure there is a lot more wisdom on this topic from other leaders out there. So, please share what you have found helpful.

Where Can You Find Growth (Part 2)?

We are continuing our two-part series on where leaders should focus for growth in a changing world that is full of new technology. This post builds on Part One, which covered major trends, the need for customer insight and what is required to manage your data effectively.

Our attention turns again to your customers — but this time also considering the issue of their irrational behavioral biases. How should this human trait influence your plans or focus for growth?

With irrational customers, what should you do?

With the Financial Conduct Authority (FCA) focused on behavioral economics (BE) and expecting providers to take it into account, the days of assuming customers will act rationally are numbered.

I’m sure most of you have at least heard of BE. The success of popular books on the subject — from the easy to read “Nudge” to the slightly more challenging “Thinking Fast and Slow” have ensured that there has been plenty of media coverage and social media debate on the implications and appropriateness for policy and action.

See also: How to Take a Bold Approach to Growth  

As with many academic disciplines, different experts use slightly different nomenclature to order the different irrational behavior or biases observed. However, for financial services clients, a good place to start is the list of 10 biases published by the FCA. My own experience in helping clients test communications or design marketing to take irrational biases into account suggests this list covers the bases.

Do you test your communications?

Of course, the focus of FCA regulatory action is ensuring the customers receive positive outcomes through products and services suitable for their needs. Unfortunately, some agencies offer to help businesses understand and act to protect customers from BE biases by seeking to “rubbish” traditional research or the role of customer insight teams. This is so misguided. Most successful BE projects require well-designed research, as well as behavioral analysis, data capture and database marketing skills in experimental design. In other words, it is probably your existing customer insight team that is in best place to take such work forward.

Given that most firms focus first on ensuring their communications could not be accused of manipulating biases, two biases (in particular) are worth considering:

  • Framing, salience and limited attention: Is the bias such that different decisions are made if information is presented/structured differently (as sommeliers know well).
  • Present bias: Is the present over-valued compared with the future (i.e., I would accept a smaller payout now, compared with delayed gratification with better return).

Still, other biases matter and occur from time to time. For a fuller list, see this previous post summarizing all 10 biases.

Conclusion

There are many different and exciting innovations happening, including the use of blockchain, robotics, virtual reality and machine learning. But, having seen those innovators who go on to thrive and those who do not, I am making the case to focus on people — not technology.

Developing a strong customer insight capability that is supported by well-managed data and is used to guide all interactions with customers is a sustainable route to growth. However, to achieve both customer loyalty and the approval of regulators, you will also need to consider irrational customers.

We are practically in a “seller beware” market, so, to truly protect your business, make sure you know (better than your competitors) how to help your customers achieve positive outcomes. Oh, and learn how to tell them what you know in their language.

See also: Does Your Culture Embrace Innovation?  

Such a human-centered-design approach to business is not easy, but it is fulfilling. Focus on understanding and serving your customer better. When you have a compelling story to tell, you will also be able to mobilize one of your biggest weapons. That, of course, is all the people who work in your business.

To modify the oft-quoted line by President Bill Clinton about what matters most: “It’s the people, stupid.”

The Hidden Issue in Facebook Dispute

Headlines can have direct bearing on the world of data and insight —and this has been even more frequent in recent years. This, increasingly, including topics like data monetization.

One such story was the news that Facebook was preventing Admiral Insurance (in the U.K.) from using social-media-activity data as a means of assessing risk. Admiral planned to enable Facebook users to not only log on with their Facebook ID but to also opt in to giving Admiral access to their data in return for potentially lower car insurance premiums.

Given the higher cost of car insurance for younger drivers, the idea had real appeal.

However, it appears that, at the last minute, Facebook announced that it is not willing to allow such data from its users to be shared with Admiral, citing data privacy concerns. (If you missed it, the full BBC news report is here.)

Why should such news matter to customer insight leaders? This dispute gets to the heart of a new battleground for both service providers and those collecting significant amounts of user-provided and user-generated data/content.

See also: 5 Predictions for the IoT in 2017  

The issue at stake

Sadly, this appears to be another example of today’s data barons relying on an old-style command-and-control mindset. To reach the potential for greater data democracy, we need to see a move in corporate culture toward greater collaboration and transparency.

We may never know the rights and wrongs of Admiral’s negotiations — like whether or not it was naive in failing to contractually lock down access to the required APIs. However, Facebook’s behavior still appears to be heavy-handed and conveys an arrogance in regard to data ownership that is disappointing. But then, perhaps, the leopard, which thought it was fair to experiment on users without permission, has not really changed its spots.

It is an interesting object lesson for other firms aiming to create value from social data and data sharing between businesses.

Customers should own their own data

The core of my concern, however, comes from a customer perspective. As more and more firms — from Admiral to TripAdviser — are looking at data-monetization plans, firms should remember whose data it is. Hiding behind fine words about protecting privacy does not mask how consumers are being denied decision-making power about their own data.

It is my hope that truly customer-centric organizations can learn from this bad example. People deserve to be educated about the reality of data monetization in our changing world. Many applications, with permission, will have the potential to make peoples’ lives easier or to save them money (for the price of their data).

Infantilizing customers by deciding what is to be allowed is “Nanny State” thinking. What our industry and our society needs, instead, is clear communication that gives people the opportunities and choices of what should happen with their data. I suspect many young drivers would have chosen to share their Facebook data with Admiral in return for cheaper premiums.

Changing mindsets, thinking in terms of customers as more active data owners, also happens to be the best mindset to adopt in preparing for GDPR.

A controversial issue

It has been interesting to see how this Facebook-Admiral item has divided opinion.

The active hub My Customer promptly ran an opinion piece (to which I contributed toward the end of the article). As you can see, there are strong views on both sides.

See also: How to Turbocharge a Marketing Budget  

I see the need to raise awareness about social media data being used by other companies. Too many conferences laud the potential of big data without an equal emphasis on data protection and permission-based marketing.

But I still come down on the side of giving the customer the choice.

Closing reflections

Let’s just reflect on the fact that this might be an example where a U.S. tech giant is not embracing the free market and the U.K. insurer could be the customer’s champion.

Strange times, indeed…

Let us know if there are other news stories that have grabbed your attention or on which you’d like to know the Customer Insight Leader view.

Missed Opportunity for Customer Insight

Customer insight (CI) teams can take different forms in different businesses (partly rightly, to reflect the needs of that business). One such variation is reporting line. Some CI teams report into operations, sales, IT or even finance. However, by far the most common reporting line is into marketing.

See also: 3 Skills Needed for Customer Insight  

That makes sense to me, as over the years I have seen more and more applications for customer insight across the marketing lifecycle. Increasingly, marketing teams are realizing that use of data, analytics, research and database marketing techniques is part of their role. Sadly, these technical teams are, too often, still separated. But at least there are signs of collaboration.

Marketing Automation:

Companies and leaders also recognize different applications of insight to marketing. Some focus on early-stage roles in strategic decisions, some on proposition development and some on campaign execution or marketing measurement. Very few appear to use customer insight in all they do.

Meanwhile, one of the trends of recent years has been the adoption of marketing automation systems. In some cases, the term has almost been used to replace the infamous customer relationship management (CRM) system. But, for many businesses, it is more about bringing a structured workflow, resource management and quality controls to the work of marketing teams. Talking with consultants who specialize in helping businesses implement marketing automation systems (none appear to work straight out of the box) reveals a sadly lacking focus on customer insight.

This is such a missed opportunity. The marketing workflow needed by today’s business requires input, validation, targeting or measurement at almost every stage. But it seems that marketing automation designs are not routinely embedding customer insight deliverables into marketing processes.

Regulation:

It is perhaps surprising that more focus has not been put on automating routine use of insight in marketing, given the regulatory environment.

Whether you consider certain vertical markets (like the role of the Financial Conduct Authority), or the higher hurdles coming to all data uses (with the adoption of general data protection regulation, or GDPR, principles), marketers will need more evidence. Those data marketers keeping up-to-date with their professional responsibilities will realize they need to evidence suitability of their offerings, targeting of their communications and appropriate use of data.

Where’s the gap?

So, in what parts of the marketing lifecycle are marketers neglecting to use customer insight? Where are the most important gaps?

Based on my consultancy work, often helping companies design their customer insight strategy, I would identify the following common gaps:

Participation decisions:

  • Either not having a clear understanding of market segments, or not making participation (product categories or distribution channels) based on segment fit or size of appeal.

Communication design:

  • The use of insight generation has grown for product design (as per our recent series), but too few marketing teams also use that same insight generation to design their communication.

Communication testing:

  • Quite often this is left to ad hoc qualitative research, with insufficient use of techniques like eye-tracking or quantitative experimentation at concept stage.

Event triggers:

  • Identified as important to targeting in two recent research reports, from the DMA & MyCustomer/DataIQ, event triggers deserve to be more widely used in targeting marketing campaigns. For further thoughts on why you don’t just need propensity models, see previous posts on both events and propensity models.

Holistic marketing measurement:

  •  As more and more marketing directors are expected to report on their return on investment (ROI) or return on marketing expenditure (ROME), once again insight can help. Not just the traditional role of database marketing practices, in reporting incremental return against control groups, but also, increasingly, the design of holistic measurement program (converging evidence from brand tracking, econometrics and other data sources). This previous post shares some more detail on that.

Will you be insightful or ignored?

In closing, I’d encourage all customer insight leaders to get closer to those leading marketing in their businesses. Marketing will become increasingly challenging over the next 12 months. CI leaders have the potential to become trusted advisers who can support marketing directors in navigating those choppy waters.

See also: The 4 Requirements for Customer Insight  

To return to the theme of regulation. I once more advise readers to not underestimate the potential impact of the EU’s general data protection regulation (GDPR) on their businesses. Despite Brexit, every commentator seems to agree that this regulation will affect U.K. businesses. The most eye-catching element may be the scale of potential fines (as much as 4% of global annual revenue), but the changes to consent may affect marketers more. The new hurdle will be proving positive unambiguous consent. Many businesses may conclude they need to move to opt-in for all marketing content.

So, going forward, the biggest threat to marketers (those not embedding insight into their processes) may not just be losing customers. It may be losing the right to talk to them!