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Blog Posts

We can predict the future

July 11, 2017

Sam Richardson

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A bold statement indeed, but not unjustified. Within the context of big data, it is conceivable that sooner rather than later, we will be able to accurately predict the future in big business. And here’s why.

Big data has come of age… Or has it?

As many people reading this will be aware, much has been written about big data in recent years in general, and its promise to revolutionize big business in particular.

Such is the power and pervasiveness of big data that in 2017, the big data analytics industry, dedicated to helping big businesses leverage petabytes of information now generated, is worth $122 billion, and still growing.

The basis of the big data promise is that extremely large data sets may be analyzed computationally to unlock hitherto unfathomable patterns, trends, and associations, especially relating to human behavior and interactions. And that this hidden insight would provide killer competitive advantage.

Diverse, disparate & messy data go into big data analytic tools, and actionable insights come out.

But so far, big data has somewhat disappointed in delivering killer competitor advantage.

All style and no substance, maybe

A common criticism is that big data analysis is often shallow compared to analysis of smaller data sets. In fact, in many big data projects, there is no large data analysis at all – the challenge is the extract, transform, load part of data handling. Thus big data has become pure process, and the meaning-making is lost.

In our experience, up to 80% of investments are spent organizing and structuring data – only to find that the analytics produced are not useful to the business. And having invested in purpose-built tools to analyse data at scale, businesses have often simply bought stylish interactive dashboards that visualize it.

Big data in and of itself is not enough

In reality, big data analysis must be situated in social, economic, and political context. But as companies invest large sums to derive insight, less than 40% of employees have sufficiently mature processes and skills to do so.

The Google Flu Trends project is often cited as an example of the failure of big data. The algorithm aimed to estimate the prevalence of real-world flu cases based on Google search queries trained on historical data about both. Initially it performed well, but was soon wildly over-estimating the number of cases. Machine-learned algorithms are supposed to get better over time, not worse.

GFT (Google Flu Trends) and other big data methods can be useful, but only if they’re paired with small data – traditional forms of information collection. Put the two together, and you can reach an excellent model of the world as it actually is.

Further, the use of multivariate methods that probe for the latent structure of the data, such as factor analysis and cluster analysis, have proven useful as analytic approaches that go beyond the bi-variate approaches (cross-tabs) typically employed with smaller data sets.

And this is where it gets interesting.

Predictive insight – accelerated performance

Within the big data conundrum, Nepa focuses on “predictive insight”. Predictive insights are an insight and a forecast. We use predictive rather than descriptive analytics to form repeatable insights, producing improved performance and optimizing ROI. Output is always “the next best action”, delivered as short and long term impacts to our clients’ business.

4 crucial factors for successful big data projects

When we undertake big data projects for our clients, we always stick to 4 proven parameters:

  1. Success with big data is starting small. Business value is the end goal. Pick a business challenge and build only the necessary to prove value. Be successful, fast! Grow with the next question.
  2. If you’re not keeping score, you are just practicing. Set clear success metrics based on a solid understanding of where decisions are made and the criteria they are based on.
  3. Working lean and iterating. Work with sample sets of data. Try new different methods. Validate with business decision makers and tweak if necessary.
  4. Projects are led by someone fluent in business and data science. Most data scientists come from “outside” of business. Few people in business speak data science. Successful programmes are led by people capable in both.

In staying true to these principles we provide the right type of insight, to right person at right time.

A case in point – companies expect good advice, again and again and again

In a big data project for one of our retail clients, a key business challenge was that the data structure for the direct-to-consumer business was not being used to its fullest potential. Output tended towards the simple descriptive, rather than the prescriptive. And diagnostics combining a breadth of multiple data sources to evaluate store performance were not easily available.

Our solution was to combine their data sets, and apply advanced data science modelling to generate holistic and predictive insight.

Data sources ranged from the internal, e.g. sales data, store & street traffic and product assortment as well as distribution numbers, to the external, e.g. weather patterns, and industry and consumer trends.

Our output consisted of:

  • Mid to long term strategic guidance;
  • Consulting with senior management in their decisions, including discount planning by region and store;
  • Long-term staff planning;
  • Optimal location for new stores;
  • Stores eligible for closing, to increase overall profitability.

All with the aim of optimizing profit across the business.

Big data needs big judgement

According to an article in the Harvard Business Review, big data must ultimately be complemented by big judgement for best effect, and that’s how we predict the future for our clients.

It’s our killer competitive advantage, that we make yours. So, why not get in touch with me and let´s start to predict your future.

Gabi Clark
Head of Insight at Nepa UK

Blog Posts

Talk is cheap – get better and more predictive insight

May 31, 2017

Sam Richardson

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To quote David Ogilvy:

“The trouble […] is that people don’t think how they feel, they don’t say what they think and they don’t do what they say.”

This is a challenge we have always faced in the insight industry, if we are really, very honest with ourselves. And we are very conscious of this at Nepa. But in recent years, behavioral economics has done much to advance the understanding that what people say is often quite different from what they actually do, when interrogating their decisions.

Let’s be clear though. It’s not that people set out to consciously deceive or obscure the facts of their decisions and subsequent behaviors in typical primary research scenarios.  It’s more often:

  • that they don’t know or can’t articulate what they prefer, what motivates them or which choice they think is best;
  • that they can’t remember;
  • that they change the way they feel about things from one day to the next.
  • and so on.

And therein lies the crux of the matter.

We are more emotional than we think we are

We do not always make logical, considered decisions when we think we do. Almost the entirety of what happens in our mental life is not under our conscious control. And this is the irony of asking people to “think about how they feel” about something.

Direct questioning techniques, measuring explicit response – e.g. thinking about feeling – assume logical steps in the decision making process that lead ultimately to, and might even predict, people’s behaviors.

This reflects what Daniel Kahneman calls a system 2 processing modality. Kahneman highlighted that this processing modality is slow, rational, analytical and involves effort.

But – it is less used in day to day decision making than we might imagine.

What he goes on to explain is a second processing modality he calls system 1. This is a wholly different approach to decision making based on gut feelings. And he attributes much of our decision making to this modality. It is fast, instinctive, intuitive, automatic and rooted in emotions.

Kahneman says that system 1 thinking is influential, guiding and steering system 2 thinking – to a very large extent.

This system 1 processing modality, if one accepts that it is nearer to the truth of day to day decision making than say the processing modality of system 2, has profound implications for how we collect and interpret data in primary research scenarios.

Adapting to “thinking about how people feel”

At Nepa we know people may not be able to tell us even half of what actually leads them to make their decisions. We know we should not just use direct questioning techniques, we should add system 1 style implicit research techniques into our tool box to help measure these “gut” and instinctive decisions – that ultimately dictate whether and what we buy.

We have developed a new data collection methodology

As a way to provide better insight into what is actually driving behavior – not just what people tell you is driving behavior – we have developed a new data collection methodology, sympathetic with system 1 modality. With this advanced technique, we get much better diagnostic results.

Our system 1 approach is based on implicit reaction time (IRT), a practical, scalable technique that has higher ecological validity.

But – an important question is what can our implicit reaction time tests tell us about consumer attitudes and intentions, in addition to the feedback we get from traditional, explicit, research methods?

There are many examples in peer reviewed literature demonstrating the added value in adopting implicit research techniques:

  • Explicit and implicit measures are both good at detecting attitudinal differences between brands when the difference is large or obvious.
  • Only implicit methods detect differences when they are less obvious.
  • And importantly, research shows that implicit data collection methods used in a consumer context are difficult to fake.

Avoiding cheap talk to get better predictive insight

However, even more extensive research has shown that the greatest predictive power against consumer behaviors comes from combining conscious AND non-conscious measurement.

That’s why at Nepa we advocate using implicit response time tests and modules, alongside more traditional questioning methods, to gain greater predictive insight into a very broad range of questions, including but not limited to:

  • Who is the most effective and plausible endorser for my product?
  • How is my brand being perceived against competitors?
  • Which version of an ad is going to work best for my brand?
  • Which version of ad copy will work best for my brand?
  • Which packaging design is most effective at signalling product benefits?
  • Which logo design do my customers prefer and which do prospects prefer?

In our book, talk is not exactly cheap, maybe just a little long-winded…

To maximize the ability to act predictive and to become a true customer-centric business, you need to go a little bit deeper than just reading my blog post. So, why not get in touch with me and I promise to get your business started.

Gabi Clark
Head of Insight at Nepa UK

Blog Posts

Insights from “The superpowers of data science and analytics”

April 22, 2017

Sam Richardson

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Using the right data for the right purpose

Earlier this month, Nepa the good fortune of attending the Marketing Analytics and Data Science Conference in San Francisco (April 3-5), an annual rite of passage for marketers who are already sold or hoping to be sold on – as the conference itself boasts – the “superpowers of data science and analytics.”

As a proud benefactor of these superpowers, the tone of the conference was both familiar and validating, especially as Nepa strives to create value for its clients at the intersection of big data and data-drive consumer insights.

The conference was chaired by David Boyle, Executive VP of BBC Worldwide, and featured esteemed speakers from Academia (Harvard, Carnegie Mellon), a variety of verticals (Netflix, Politico, AMEX, Schwan Food Company) and – as San Francisco is among the most popular cities for new businesses – the start-up community (Sam Yagan, Bloomberg Beta).

Nepa, of course, took copious notes throughout MADS17, so it’s our pleasure to now reveal some of the bigger takeaways from the conference:

Prepare and then ask the right questions

  • Alistair Croll, of Harvard Business School, spoke eloquently about how marketers once asked a question before they acquired data to (hopefully) answer it. The new answer is not to build a machine prepared for all questions, but to build a data infrastructure by answering all known questions the business has.
  • Or, as Bill Franks, CTO of Teradata, succinctly put it in 2012: “To succeed with big data, start small.” Croll also shared research from Microsoft researcher Cormac Herley about the infamous Nigerian prince email scam, which we’re certain no attendees ever fell for. What everyone wants to know is why anyone would fall for it, when it’s proffered with such ridiculously unintelligible language. Sure, if the English were at all coherent, click rates would improve, but as recipients got closer to making a transaction, they would realize the scam and, have wasted the time of the crooks. The implausible story, by design, only attracts the most gullible people, those most willing to see it through for the entire scheme.

The lesson is to never tacitly accept the metrics. We see this in the focus on CPM for ad sales or focus on channel sales at omni-channel retailers, question everything so your efforts are achieving maximum impact.

READ ALSO: Optimise your marketing budget with Marketing Mix Modeling

AI and Big Data are not always correct. In fact, often they’re way off.

  • Susan Etlinger, an analyst at Altimeter Group, shared some wisdom from the audience on the topic of algorithmic bias. At its core, this means the inputs into algorithms can contain bias that then have unfortunate results in the output. An example of this comes in the literary world: A database of prose will have predictive models believing there are many more black sheep than white sheep because the former are discussed more in literature, even though they’re significantly rarer in reality. Those living in rural areas know what we’re talking about.

Lesson here: Be mindful of the data sources used and their potential biases. Side note here – with speakers from BBC, Politico, Civis Analytics, and more – there was no lack of chatter about the 2016 election – where there were many biases in data that led to flawed prediction models.

Optimizing without customer experience is dangerous.

  • Nepa embraces the power of data science and analytics, but get concerned when the focus is too much about operational efficiency. David Rogier, founder of MasterClass, shared an example from Tesco where the grocery chain was able to optimize a store so efficiently that it could run with four employees per shift compared to the usual 30 plus for the same sized store. They achieved this largely by having only self-checkout options and laying out the store for fastest restocking. The problem was that, over time, there were no customers!

But why? As Nepa has learned using experience data, reducing the cost of labor, lowering inventory and driving more efficient use of space can improve operational performance, but it will take its toll on sales and traffic if drivers of customer experience are not accounted for.

Presentation and education matter.

  • Rebecca Haller, Director of Audience Insights at POLITICO, shared some words of wisdom learned from her early writing days: “Journalists don’t ride the bus enough.” It’s a nice way of saying reporters are out of touch with the people they’re serving.
  • Josh Hendler, CTO of Purpose, shared that when he was working with field teams he didn’t really need more data analysts, only those who could educate people on the available tools and how to use them.

These two points are very applicable to the research and analytics industry, where insights are organized and curated with headquarters or the c-suite in mind, not the people on the front lines that can act on them.

READ ALSO: Benefits of Path to Purchase analysis

And, to wrap

I think one of the key themes about data science and marketing analytics was on display in a presentation delivered by Haile Owusu, Chief Data Scientist at Mashable. When it comes to data there are “haves” and “have nots” – i.e. companies that have first party data (retailers, OTT apps, etc.) and those that don’t (CPGs, content creators, etc.). For the “haves” – this can be used to significant competitive advantage (e.g. Facebook, Amazon, Walmart, etc.) – though, unfortunately, many companies do not take advantage of this opportunity (omni-channel retailers come to mind).

For the “have nots” – Haile Owusu reminded us that not all hope is lost – his team uses data fusion and robust analytics – to predict which pieces of content will go viral and where. These insights help them to optimize their investments to support the content with greatest potential. Through advancements in data engineering and data science fields, the data “have nots” are able to bridge data sources and use them for market advantage.

Thanks to Knect365, David Boyle, the presenters, and attendees. “MADS17” was a great conference and we look forward to MADS18. Hope to see you there.

What about Nepa?

If you’re not keen on spending valuable time refining methodologies, sitting behind one-way mirrors, and waiting on crosstabs, all before it’s all analyzed into insights with fleeting impact, then Nepa is someone to speak to.

Sean Dunn
Vice President, Client Solutions at Nepa USA

Blog Posts

Transform data into insights – boost your innovation capabilities

March 19, 2017

Sam Richardson

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Generate the insights that are relevant to you

I get a lot of questions on how to handle all data (or Big Data) and particularly how to use the insights for something that creates business value. Not only in the distant future but here and now.

Very often I sense great frustration and nervousness related to this question. I have previously identified 5 crucial steps to develop a customer feedback system to drive innovation. There is definitely reason to delve deeper into these steps. Let´s start with the first one:

“Understand the difference between data and consumer insights”

I would say that all of us know that we have a lot of data. We also know that we could benefit from using it more often and more targeted. Most of us also know that it’s one of many requirements of future business success. The problem is that for many managers the task feels unsurpassable.

Questions in abundance stacks up:

  1. Where should I start?
  2. Whom should I listen to?
  3. What platforms do I use?
  4. What BI tool should be employed?
  5. Should I have a cloud solution etc, etc?

The more questions you try to answer, the more questions pop up.

A popular response is to do something because it’s better to do something than nothing, right? Well, maybe… but you don’t delve into the technical issues until you have a clear understanding of what you want to achieve – that there are infinite possibilities as to what you can do, but a lot less that you should do.

READ ALSO: Benefits of Path to Purchase analysis

How can business leaders transform big data into something resembling insights?

In today’s world of information technology and exploding social media interaction, data is abundant. According to IBM we create 2.5 quintillion bytes of data (that is a lot of zeros, in fact it’s 2 500 000 000 000 000 000 if you were to write it out in numbers, or 1018) per DAY. (

“We create 2.5 quintillion bytes of data per day”

This is so much that 90% of the data in the world today has been created in the last two years alone. That’s BIG DATA … and if you think that the amount of data will decrease, think again. With IoT and 5G, this is just the beginning.

Only convert data into insights your business can use

In that abundant wealth of data, an overall problem is that there are even more insights than data points that can be harvested. I would argue that the number of insights available is already on the brink of infinity. The trick is to generate the insights that are relevant to you, your business and your market – today and in the future.

A mistake many data scientists and analysts make, is to try to solve many problems at once. Having the skills and finding the ultimate machine that can answer everything, every time, everywhere is like finding a pot of gold at the end of the rainbow. It just can’t be done. What you must do is to focus and to quickly convert data into insights your business can use. I have successfully used a use case approach to reach those goals.

A use case helps you solve a specific business question by using data and technology solutions. A good use case provides insights that are relevant and on time to the relevant stakeholders to support them in their decision making.

READ ALSO: Get to know your customers with brand health tracking

This is the playbook I use: A use case approach in 8 steps

  1. Identify business driven use cases, questions or challenges that business managers need to handle now and in the near future. In terms of innovation, identify which parts of the innovation process that needs insights – what, when, and why decisions that need to be supported. Is it innovation or is it about product development?
  2. Get your business people to prioritize between use cases from a business perspective, i.e. which use cases will give the most significant impact on your business?
  3. Understand what data is available and where. It is a simple enough statement, but a task that can be daunting. Often there are many different sources such as data warehouses, providers, etc. If the task feels overwhelming, let the prioritized use cases guide you to what data should be investigated.
  4. Do a feasibility analysis, i.e. soberly evaluate resources (time, money, etc.) needed to solve each use case. Don’t go into details yet, do a high-level analysis.
  5. Create a priority matrix – map business value versus implementation cost. It´s not uncommon for a second or third priority, from a business perspective, to be the first use case that makes sense to implement based on a trade-off between feasibility and business value.
  6. Create a roadmap based on the priority matrix.
  7. Build the first use case solution, start with a MVP (Minimum viable product). A MVP:
    1. has enough value that people can understand, use, and buy in
    2. demonstrates enough future benefit to retain early adopters
    3. provides a feedback loop to guide future development
  8. Show business value to get traction within your organisation, then move on to next use case, show business value, iterate, iterate, iterate…

READ ALSO:  The brand metrics that actually matter

Never underestimate the need for communication – learn from a Nobel prize winner

A word of caution: As data driven, logical, and analytical people we tend to think that data is king; in some sense, that’s true. But a human mind works differently. As Nobel prize winner Kahneman (,_Fast_and_Slow) identified, we use two different modes of thought:

  1. System 1, which is fast, instinctive and emotional (The Hare)
  2. System 2, which is slower, more deliberative, and more logical (The Tortoise)

Research has shown that the Hare is a lot more dominant than the Tortoise in human decision making. That is why humans (and yes, managers are humans) are very reluctant to believe insights that contradicts their gut feeling. That’s why it’s important to include hypothesis sessions, i.e. what do the managers believe the data will tell them, when working with customer insights in general, and with Big Data specifically. Because, when you know the Hare, it’s easier to be the Tortoise.

Furthermore, you need to set up a structure with communication plans, steering groups, and all that management mumbo jumbo. In the end, if you don’t get the HiPPO (Highest Paid Person’s Opinion) to sponsor you, then you will be another frustrated fish swimming around in the Data lake.

Even though this post only touches on surface of all the implications of turning data into insights, I hope it has helped you navigate in the choppy waters of Data Lakes and Big Data. If you have any questions or comments, please don’t hesitate to contact me.

Erik Enecker
Chief Product Officer at Nepa USA & COO Global Product at Nepa

Blog Posts

Innovation and the power of consumer insights

February 21, 2017

Sam Richardson

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Businesses that thrive are investing in continuous innovation

powered by continuous consumer insights. They don’t sit and wait with the risk of losing ground. As Nokia CEO Stephen Elop tearfully said, after announcing his company’s acquisition by Microsoft, “We didn’t do anything wrong, but somehow, we lost”. It was precisely this lack of action, the failure to innovate, that resulted in the downfall of Nokia. Yet with another mindset and the tools available today to drive business innovation the smart way, there’s no excuse for your business to get left behind.

Today’s consumers tend to change their behavior very often and unexpectedly. That’s why it’s more important now than ever that your business evolves to catch up. You can start by looking for opportunities to innovate based on the 3 (really basic but nevertheless crucial) characteristics.

3 basic characteristics of a true business innovation:

  1. An idea that solves a need in the market in a new way
  2. Meets this need in a way customers are willing to pay for
  3. Can be transformed into a product or service available on the market

READ ALSO: Brand tracking – your most valuable marketing tool

Innovation can be like sawing off your own legs to grow

New innovation hurts. It is often disruptive and can even hurt your business in the short run. It’s a bit like sawing off your own legs in order to grow newer and stronger legs over the course of a few years; and many businesses aren’t willing to take this perceived risk when quarterly earnings are at stake. So what do they do? They wait and hope, and then watch other innovative companies outpacing them gracefully, as they are sprinting ahead with newer, stronger legs.

Take for example Kodak, Blockbuster, and Nokia. Giants of their industries at the time, each of these business once held a powerful opportunity to innovate; but they all failed to take the chance. Kodak had the patent for digital cameras, Blockbuster was given the chance to buy Netflix, and Nokia could have moved into smartphones; but they didn’t act. Why? Because they were afraid. Afraid it would hurt too much, hurt the quarterly earnings, hurt stock options, etc. So someone else did it instead. How did that work out?

READ ALSO: Brand research: What is brand research & why is it important?

5 areas where customer insights will make a big impact on your business

I would say that there is almost no area in your business where you should not let the voice and the footprints of your customers influence why and how you choose to develop things. Here are 5 areas where I currently help our clients develop their business:

  1. Assessment of opportunities
  2. Ideation based on customer insights
  3. Brand development
  4. Concept and offer development (that this article is mostly about)
  5. Marketing and advertising development – where “detail” is king

But let’s get back to talking about innovation. Can big businesses innovate? Of course they can! They have the resources, industry expertise and distribution at their disposal. They just need the right mindset, tools, and the right people.

The 5 pillars of business innovation that every business can use to succeed:

  1. Acquire the right talent and focus on the people in your organization.
  2. Create the right environment. Business managers tend to minimize risk, optimize processes and make changes with extreme caution. Most of them are afraid to fail, ending up by doing nothing, until doing nothing is a bigger risk. That’s a poor environment for innovation.
  3. Be opportunistic. To unleash the innovative power of your business, you must be willing to take risks, go outside of normal processes, and prototype new ideas.
  4. Let go of control; incentivize instead. The only way to do that is to give the entrepreneurs in your organization freer reign. Manage them with incentives and not rules, processes, checks, and balances. Managing innovation in a controlling way will never work because, per definition, real innovation is a high risk venture with many uncertainties. If you manage real innovations like “a normal project”, then you will fail.
  5. Create a system for continuous customer and market feedback to understand the real habits, rituals, and beliefs that will empower your organization (the lifeblood of an innovative business). It’s hard to make your customers change their behavior. Apple and Procter & Gamble, for example, make a point of engaging customers directly to generate new ideas. They develop new products and services based on superior end-user understanding.

READ ALSO: Brand tracking is key to increase brand awareness

5 crucial steps to develop a customer feedback system to drive innovation

To develop a system to generate continuous customer and market feedback, which will drive innovation, you need to ensure a better fit for your products/services and reduce time to market.

  1. Understand the difference between data and customer insights. Behavioral data often gives you tons of information about What, When, Where, and How, but not so much about Why. Customer insights on the other hand is all about the drivers, the motivation, the unmet needs, the concerns, and the wishes; the fuel for your innovation and your marketing and development opportunities.
  2. Gather customer data from multiple channels, including feedback and actual behavior (the footprints of your customers). Use this data to identify needs your innovations can solve… and do this continuously.
  3. Change the way you work with innovation. Apply a much more agile approach. Interact with your customers through the whole process from early ideas to launchable products.
  4. Collect data on your actions as well as your competitors’ actions to learn from successes and failures systematically.
  5. Test and learn, test and learn, test and succeed. There is nothing wrong in failing as long as you learn from your mistakes. This is how the masters of innovation does it.

I help businesses to thrive with innovation by utilizing the power of continuous consumer insights. There is no end to what you can achieve with real-time consumer insights, customer feedback, and footprints. It changes the way your business thinks about innovation and you avoid the risk of becoming another Nokia. In this post I have only scratched the surface. Why not get in touch with me and I promise to let you in more on the secrets and get your organization started.

Erik Enecker
Chief Product Officer at Nepa USA & COO Global Product at Nepa