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Are Smart Things Really Smart?

Last week I wrote a long post on “The 7 Essential Differences Between Simply Responding to Customers and Providing True Customer Service“. So this week I wanted to write a shorter thought piece on a topic getting a lot of airtime these days; that of smart things, the IoT or the Internet of Things.

We seem to be surrounded by smart things: smart watches, smart clothing, smart cars, smart houses and smart appliances. My question to you is “Are they really smart?” (>>Tweet this<<) 

The reason for my question is that I recently read an article entitled “Taking ‘Smart’ Out Of Smart Things” by Chuck Martin. It made me think about whether “smart things” really are that smart or whether it’s something else that’s making them appear smart?

So here are my views on it; feel free to add your own opinions in the comments below, I would love to start a discussion on “smartness”.

The Age of the Customer and the Fourth Industrial Revolution

According to Forrester in their report published early last year, entitled The Business Impact of Customer Experience“, we are now in the “Age of the Customer”. This was music to my ears when I first heard that, because as you know I’m a customer champion. However, at the beginning of this year, The World Economic Forum reported that we are now on the brink of the Fourth Industrial Revolution“. (>>Tweet this<<)

In their article, they explain that “The First Industrial Revolution used water and steam power to mechanize production. The Second used electric power to create mass production. The Third used electronics and information technology to automate production. Now a Fourth Industrial Revolution is building on the Third, the digital revolution that has been occurring since the middle of the last century. It is characterized by a fusion of technologies that is blurring the lines between the physical, digital, and biological spheres.”

Does this mean that people will have less and less importance as technology takes over more and more areas of our daily lives – and value? Luckily no. The author, Klaus Schwab, Founder and Executive Chairman of the World Economic Forum, concludes the article by saying “In the end, it all comes down to people and values. We need to shape a future that works for all of us by putting people first and empowering them. In its most pessimistic, dehumanized form, the Fourth Industrial Revolution may indeed have the potential to “robotize” humanity and thus to deprive us of our heart and soul. But as a complement to the best parts of human nature—creativity, empathy, stewardship—it can also lift humanity into a new collective and moral consciousness based on a shared sense of destiny. It is incumbent on us all to make sure the latter prevails.”

So no panic; there will hopefully still be a place for people in this brave new world! But that doesn’t mean that we won’t have to adapt – and adapt quickly if we don’t want to be left behind.

In researching for this post I also found that “smart” is now being attributed to many, many new areas. And this is thanks to the increased use of data or as we now like to term it Big Data. Data tells us what to do, or more precisely, computers control the processes in which we are involved. Although humans are still smarter (for now?), machines can tell us things that we didn’t know or couldn’t work out for ourselves, or if we could, not as quickly. Read more about this here.

Smart Things or Lazy People?

Another aspect of smart things is that they make life easier for the user. This might suggest that people will become lazier if they don’t compensate for all the actions they no longer make during an average day. Of course, those with fitness b ands, like myself, may just continue to do things manually for the increased statistics on our activity counter; this article makes a good read on the topic. But the average “Jo” will add more and more robots to do the manual work that they don’t want to do, so what will they do with all this new-found leisure time? Work more or play more? My bet, or rather hope, is for the latter!

With machines taking over the more menial tasks, we will be forced to make better use of our brains; after all, it’s the only thing that robots don’t have – for now at least! So are you training yourself to think more, improve your memory and polish up your analytical skills? According to Korn Ferry’s 2015 Pulse report, a customer centric approach and analytical skills are the two most sought-after specialized skills within the marketing function today. (>>Tweet this<<) So if you’re in marketing you’d better start honing them, before the robot takes your seat! Smart people will realise and take action; the less-smart will wait and see. Which are you?

What do you think? What challenges and opportunities will smart marketing bring us? Please add your comments below and let’s start a wave of smart discussions!

Winning Customer Centricity BookThis post includes concepts and images from Denyse’s book  Winning Customer Centricity . 

It is now available in Hardback, Paperback, EBook and AudioBook formats. You can buy a copy from our website here, as well as on Amazon, Barnes and Noble, iBook, iTunes and in all good bookstores. Discount codes are regularly published on our private  FaceBook Members group – why not ask to join?

 

 

Market Research, Business Intelligence & Big Data: Have we Forgotten about Human Data?

The annual pilgrimage to the ESOMAR Conference took place last week in Dublin. I heard that there was much discussion, both on and off the stage, about Big Data and the future of market research. Hopefully, the whole profession will get behind one initiative, instead of each individually trying to “solve world peace” on their own!

This week sees the second Swiss BI-Day taking place in Geneva and there will no doubt be similar discussions about Big Data and the future of Business Intelligence.

It appears that Big Data is not just a buzzword or a commodity that has been likened to oil; it has become the centre of a power struggle between different industries. Many professionals seem to be vying for the right to call themselves “THE Big Data experts”.

This got me thinking about the future of data analysis in general and the business usage of Big Data more specifically. There seems to be no stopping the inflow of information into organisations these days, whether gathered through market research, which is proportionally becoming smaller by the day, or from the smartphones, wearables and RFID chips, that get added to every conceivable article, more generally referred to as the IoT (Internet of Things). Who will, and how are we to better manage it all? That is the question that needs answering – soon! (>>Tweet this<<)

Data Science Central published an interesting article earlier this year called “The Awesome Ways Big Data Is Used Today To Change Our World”. Already being a few months old probably makes it a little out-of-date, in this fast changing world we live in, but I think it still makes fascinating reading. It summarises ten ways that data is being used:

  1. Underst anding and Targeting Customers
  2. Underst anding and Optimizing Business Processes
  3. Personal Quantification and Performance Optimization
  4. Improving Healthcare and Public Health
  5. Improving Sports Performance
  6. Improving Science and Research
  7. Optimizing Machine and Device Performance
  8. Improving Security and Law Enforcement
  9. Improving and Optimizing Cities and Countries
  10. Financial Trading

Many of these are not new in terms of data usage nor business analysis. What is new, is that the data analysis is mostly becoming automated and in real-time. In addition, the first and second items, which were largely the domains of market research and business intelligence, are now moving more into the h ands of IT and the data scientists. Is this a good or bad thing?

Another article posted on Data Informed a few months after the above one, talks about The 5 Scariest Ways Big Data is Used Today   and succinctly summarises some of the dynamic uses of data today. The author of both pieces, Bernard Marr, wrote that “This isn’t all the stuff of science fiction or futurism. Because the technology for big data is advancing so rapidly, rules, regulations, and best practices can’t keep up.” He gives five examples of where data analysis raises certain ethical questions:

  1. Predictive policing. In February 2014, the Chicago Police Department sent uniformed officers to make “ custom notification visits to individuals whom they had identified, using a computer generated list, as likely to commit a crime in the future. Just one step towards the “Minority Report”?
  2. Hiring algorithms. Companies are using computerized learning systems to filter and hire job applicants. For example, some of these algorithms have found that, statistically, people with shorter commutes are more likely to stay in a job longer, so the application asks, “How long is your commute?” Statistically, these considerations may be accurate, but are they fair?
  3. Marketers target vulnerable individuals. Data brokers have begun selling reports that specifically highlight and target financially vulnerable individuals. For example, a data broker might provide a report on retirees with little or no savings to a company providing reverse mortgages, high-cost loans, or other financially risky products. Would we want our own families targeted in this way?
  4. Driving analysis devices may put you in the wrong insurance category. Since 2011, car insurance companies like Progressive and Axa, have offered a small device you can install in your car to analyze your driving habits and hopefully get you a better rate. But some of the criteria for these lower rates are inherently discriminatory. For example, insurance companies like drivers who stay off the roads late at night and don’t spend much time in their cars, but poorer people are more likely to work the late shift and to have longer commutes to work — both of which would be strikes against them when it comes to calculating their auto insurance rates.
  5. Walmart and Target determine your life insurance rates. OK, not directly, but Deloitte has developed an algorithm, based on “non-traditional third-party sources” that can predict your life expectancy from your buying habits. They claim that they can accurately predict if people have any one of 17 diseases, including diabetes, tobacco-related cancer, cardiovascular disease, and depression, by analyzing their buying habits.

Marr starts this article by very briefly discussing privacy and inherent biases in data. I think these issues are far more urgent than deciding whether it is market research, business intelligence or data scientists that are in charge of the actual data analysis. Perhaps we all need to work together so that the “Human” side of data is not forgotten? After all, most data comes from people, is understood – if no longer strictly analysed – by people, for the benefit of people, to help change people’s behaviour. What do you think? Join the conversation and let your voice be heard. (I’ll be presenting this very topic at the Swiss BI-Day this coming Tuesday, so I do hope that you will pop by and listen)

Winning Customer Centricity BookThis post includes concepts and images from Denyse’s book  Winning Customer Centricity. You can buy it in Hardback, Paperback or EBook format in the members area, where you will also find downloadable templates and usually a discount code too.

The book is also available on Amazon, Barnes and Noble, iBook and in all good bookstores. If you prefer an Audiobook version, or even integrated with Kindle using Amazon’s new Whispersync service, it’s coming soon!

Try a New Perspective on Business Intelligence: How to get More Impact & Answers

Last week I presented at the first Swiss Business Intelligence Day. It was an inspiring conference to attend, with world-class keynote speakers opening the day. They included Professor Stephane Garelli from IMD, Philippe Nieuwbourg from Decideo  and Hans Hultgren from Genesee Academy.

After such an illustrious start, you can imagine that I was more than a little nervous to present my very non-IT perspective of business intelligence. However, the presentation did seem to go down well, so I want to share with you some of the ideas I talked about. Not surprisingly, with my passion for customer centricity and always with the end-user in mind, I took quite a different perspective from that of the majority of IT experts who were present.

BI should Collaborate More

With the explosion of data sources and the continuous flow of information into a company, managing data will become a priority for everyone.

statistic id forecast big data marketThe Big Data market, which more than doubled last two years, is forecast to triple in the next four, according to Statista. BI will have to exp and its perspective, work with more varied sources of information and exp and its client base.

In the past BI was inward looking. It ran data-mining exercises, reviewed corporate performance, developed reports and occasionally dashboards. It was, and still is in many organisations, mostly concerned with operational efficiencies, cost-cutting and benchmarking.

How business intelligence fits into the data world of businessThe above plot is my own, simplified view of how BI fits into data management within most organisations today. The other three quadrants are:

  • Competitive intelligence (CI) uses external competitor knowledge to support internal decision-making. Although BI is sometimes considered to be synonymous with CI because they both support decision-making, there are differences. BI uses technologies, processes, and applications to analyze mostly internal, structured data. CI gathers, analyzes and disseminates information with a topical focus on company competitors.
  • Investor Relations (IR) uses internal data to get external people, such as shareholders, the media or the government, to support and protect the company and its views.
  • Market Research (MR) on the other h and is mostly outward looking. It studies customers’ behaviours & attitudes, measures images & satisfaction, and tries to underst and feelings & opinions. That information is then used, primarily by marketing, to develop actions and communications for these same customers.

The four quadrants, even today, usually work in isolation, but that will have to change with this new data-rich environment in which we are working.

BI is Ripe for Change

 

According to a recent (Jan 2014) Forbes article, BI is at a tipping point. It will need to work in new ways because:

  • it will be using both structured and unstructured data
  • there will be a consolidation of suppliers
  • the internet of things will send more and more information between both products and companies.
  • thanks to technology, data scientists will spend more time on information management & less time on data preparation. At present it is estimated that they spend 80% of their time on data cleaning, integration and transformation, and only 20% on its analysis!

Google glass provides access to business intelligence

In February GigaOM echoed these thoughts, claiming that we are not in BI 2.0 but rather 4.0. They said the volume of data and the number of people now exposed to it, makes data availability to everyone essential. No longer does BI involve only the CEO and IT specialists, it concerns everybody.

Google glass provides access to business intelligence
Google glass, as tested by Virgin
, is a good example of this. It delivers real-time, on time and relevant information to Virgin’s hosts and hostesses, to meet, greet and advise its passengers. Their customer support team can accompany their VIP guests and warn them of delays and gate changes as they happen. Google Glass enables them to get out from behind their desks and interact more with the guests they are trying to please.

BI must Deliver More Synthesised Knowledge

According to a recent Business Intelligence report on management’s opinion of their data, they are currently frustrated. They say that it comes from many disparate sources and is rarely if ever available in real-time. They can’t easily access it without the help of IT and it takes too long to customise it to what they need. What is particularly interesting in the findings, is that management were not saying that they don’t need information; in fact it actually looks as if they want to have access to more data. BUT more of it in a way that makes it easy to find what they want, when they want it.

Another finding from the survey shows executives’ thoughts about data delivery. Currently they are getting their information primarily through emails and spreadsheets. I find this shocking that today we still expect management to take the time to wade through all the data in order to draw their own conclusions. Less than one in eight of the C-suite is getting dashboards, which is their preferred medium (>>Tweet this<<). They also want mobile delivery so that they can access information on the go.

This study provides us with a simple plan to satisfy their needs and to help us meet our own challenges of data abundance. This is what we should prioritize, since we can no longer continue to do what we’ve always done in the same way we’ve always done it. The BI priorities are as simple as ABC; accessibility, business impact and consistency (>>Tweet this<<).

BI needs to Provide Simplified Access

Information should be provided where and when it is needed and in such a way as to have most impact on the business. This means making it easy to review, and quick and simple to draw conclusions. This is why the number one dem and from business is dashboards.

Dashboards have the advantage of imposing consistency (>>Tweet this<<) so no time is lost in underst anding what the information is showing. With the availability of more information, comes the challenge to make it available to more people. And more people will also mean more and different needs.

Business Intelligence data warehouses are like a tree of knowledgeTo underst and the accessibility challenge I find the tree is a great metaphor for what we struggle to achieve. The roots can be compared to all the different sources of information we have at our disposal. The trunk is like all the integrated information that is reported in dashboards and the branches, twigs and leaves are the different data warehouses we create.

Whilst a one-page overview is sufficient for management, others will need greater granularity. Therefore we need to make information available at different levels of detail. My experience suggests three types of information sharing.

  • The leaves are like data warehouses where the raw or nearly raw data sits
  • The twigs are the information repositories where analysed data and information resides
  • And the branches are the knowledge libraries where the integrated actionable insights sit

What I have learned from setting up numerous data warehouses, information repositories and knowledge libraries, is that it is not easy. Not because of any technical complexity, but because of winning the needed  internal support for the project and getting the essential acceptance for global access to the information. It takes more than technology, it takes a culture change in many cases too, and this is the real challenge. Stopping the “information is power” mentality means finding ways to counter the opposition who claim  confidentiality of their own data whilst also requesting access to everyone else’s. In addition, even if people need information, they will generally not make the effort to go looking for it, if there is an easier way, such as by asking someone else! All these issues need to be resolved for an integrated database project to succeed.

Business Impact

One way to encourage the culture change mentioned earlier, is to demonstrate the business impact of what you are providing. The desired impact won’t come by delivering spreadsheets, it will come from dashboards (>>Tweet this<<).

So how do you summarise a company in a one-page dashboard, especially those which are present in multiple categories, globally? Well, often the simplest way is not to try to cover the total business, but rather the top categories and markets that would cover 70% – 80% of total sales. In most cases this would be sufficient to underst and the main priorities for management.

Of course at category level each business unit should be able to get access to more detailed information, as should the regional presidents, if you are working in such a complex business environment.

The real power of dashboard information will come from data integration, where both internal and external information are synthesised, for a holistic view of the business. I have worked on several projects that combined internal information with consumer data for a complete business report. The consumer information came from promotions, call centres and CRM activities, and was combined with market research on product and communications performance, to provide a solid base of consumer underst anding. This can then be presented alongside the more usual financial information that executives are already receiving. Having a complete overview of the business has far more impact than individual, silo’d summaries and enables management to make decisions more quickly and easily.

Increase Consistency

Another challenge when setting up and integrating databases, is in the harmonisation of their master data. When you are working with consumer data, this challenge can be multiplied by ten if not one hundred. For example, consumers will talk about a pizza, without specifying the br and, sub-br and, variant, flavour, packaging and size that would be used by the business to define it. So you have to find a way to translate what the consumer is saying, into the products as recorded internally.

The consistency of the master data will even increase in importance and complexity, with the expansion in available data sources. In addition, the fact that more people will get involved, will confound things even more, since their needs will differ.

Asking Better Questions of the Data

Accessibility, business impact and consistency are vital to the success of the new BI’s data management and usage, but I feel the urge to add one more thing. That of asking the right questions of the data. Although BI is used to asking questions, I think Market Research (MR) are the real experts in questioning. Therefore they should be involved in ensuring integrated databases are combined in such a way as to permit easy extraction of whatever level of information is required, or whatever perspective might be taken.

For example, BI is used to running forecasts. Those usually start from a review of past data and current reality to develop forecasts based on complex algorithms. They will do this within their teams with perhaps input from finance. MR on the other h and, is more likely to work from societal trends and develop plausible future scenarios, brainstorming across the organisation to gather a wide array of perspectives. Both perspectives are complementary and combined, they make a powerfully readied organisation.

Making more data more accessible to more people will certainly help this question development, as I think getting the right answers depends upon asking the right question, don’t you?

These were just a few of the ideas I shared at the Swiss BI Day in Geneva. How do you see business intelligence adapting and changing as a result of the increased information availability happening today?

C³Centricity used images and graphs from Statista, Microsoft and Virgin in this post.

Solving the “Digital Experience Conundrum” for Large U.S. Banks

Being based in Switzerl and certainly has its advantages, but we are not all multi-millionaires from attracting the fortunes of foreigners, as the press sometimes likes to portray us. It was therefore a pleasure for me to receive this guest post from Bob Thompson, CEO of Customer Think, on retail banking. In it, Bob talks about the mis-match there often is, between customer and bank priorities when it comes to information integration and use. He concludes by saying that US banking is ripe for change. In today’s fast-paced world, I think the same could be said about many industries, perhaps even yours. Enjoy:  

I think one of the key issues faced by retail banks ( and indeed most retail businesses) is what I’ve dubbed the “digital experience conundrum.” This is driven by two powerful trends:

1. Consumers are embracing digital technology, via the Web and smartphones

2. Companies want to encourage this digital shift to improve efficiency and cut costs

The conundrum is that automation can reduce opportunities for more engaging, human experiences that build loyalty. And increased loyalty is a key outcome for customer experience initiatives.

Let me say up front that there are no easy answers here. Retail banks must “go digital” and the large banks certainly are. In fact, recent  PeopleMetrics research found that “customer-facing technology (mobile, digital)” and “internal technology / customer-facing processes” were ranked by banks as their first and second priorities.

Unfortunately, customers have different priorities. They ranked “products” (mainly fees and rates) and “put customer first” as their top priorities, as you can see in this slide presented by Kate Feather of PeopleMetrics at our recent  CX Forum webinar focused on Retail Banking.

peoplemetrics bank priorities misaligned

In an online poll with our live audience, 73% said they believed that the shift to digital experiences would improve customer loyalty. And Bruce Kasanoff of  Now Possible argued that banks should use technology to provide more personalized and relevant services, as you can see here.

kasanoff bank simplify automate elevate

While I agree with Bruce, I’m not convinced that technology alone is the key to driving loyalty. I think it has everything to do with how the technology is used, and the leadership and culture of the organization. Simply automating for efficiency and convenience is table stakes in banking.

What does drive loyalty? In Kate’s research, they found that community banks do a much better job creating an emotional bond in relationship where customers feel “valued, appreciated, and cared for,” as you can see in this chart.

peoplemetrics bank loyalty factors

Now, some have argued (in LinkedIn discussions on this topic) that higher NPS scores don’t really matter. Large banks are doing just fine, thank you very much, without all that touchy feely stuff offered by community banks. One comment by Serge Milman in a LinkedIn discussion group summed up this sentiment:

“Many Banks ( and Credit Unions) have been unable to convert their high customer satisfaction scores / high NPS to customer loyalty as measured by hard quantitative factors such as wallet-share, revenue and profitability.”

And indeed, in our audience poll, the top “major obstacle,” selected by nearly two-thirds of the attendees, was “ROI unclear.” Technology was No. 2 at nearly 50%.

For now, it may be true that large US banks can grow profitably without building “raving fans.” Look at their  ACSI scores  and you’ll find all the large US banks well below the industry average of 77. “All others” (which includes community and regional banks) earn a score of 79.

OK, so maybe the problem is that banks can’t be big  and engaging. They are mutually exclusive! Then how do you explain the  high loyalty scores of USAA? Somehow USAA, a very large and complex financial services business, is able to be emotionally engaging while also investing in digital technology.

My take is that large US retail banks are stuck in their old ways, and are successful enough that they don’t see the need to change. Yet.

Competitive stress could come from community banks that are being more aggressive in wooing customers with better service and higher rates. The partnership with Kasasa looks like an interesting way to shore up technology shortcomings.

Personally, I think the issue is leadership. Large US banks are doing well enough that they’ll stick with minor innovations (e.g. digital channels) around the status quo. They won’t focus on building genuinely loyal (retained and emotional engaged) customer relationships because retention is good enough.

That seems short sighted to me, but then, I don’t have a bonus riding on hitting a quarterly target. George Self said it well in a LinkedIn discussion:

The banking industry is ripe for structural change. The reinvention of banking is not many years away. I for one am looking forward to it.

Me too.

The slide images for this post were sourced from CustomerThink’s CX Forum webinar on June 6, 2013, sponsored by PeopleMetrics.  Full recording and slides are available for download here (free registration required). My sincere thanks to Kate Feather and Bruce Kasanoff for their outst anding contributions to our CX thought leadership program.

Need help in making maximum use of all your information? Let us help you catalyze your customer centricity; contact us here

If you would like to know more about working with and integrating data, whether Big or small, check out our website here: https://www.c3centricity.com/home/underst and

This post has been adapted from one that was first publised on Customer Think on June 11th 2013

The shocking truth about managing data: it’s simple!

There is so much talk about information and Big Data these days, every organisation is feeling swamped by the belief that they should be doing more with their data.

From gathering more, to analysing more, and developing more insights about their customers, they are also afraid that their competition is doing more. If this is your situation, then this post will provide the answers you need.

Organisations have always collected information about their customers; it’s nothing new. Whether this is through conducting market research studies, or simply from obtaining details when customers buy something, there is already a lot you can do today to manage your data. However, there is an even bigger opportunity to get a better underst anding of your customers and their needs and desires, when you integrate all the information sources you already have at your disposal. This is why there is so much news about Big Data these days. For all of you that have been shocked into inaction by all this talk, here are some simple ideas that you can use to start your own journey to managing your data, whether Big or Small:

 

#1. Make your information more visible

You are certainly already collecting a lot of data, both internal and external, but it is probably only the former that is shared today; sales, distribution, targets, budgets, plans and forecasts are the most common examples of this. How are all these numbers shared across your organisation? Why not develop a simple dashboard showing the most important numbers?

Using comparisons to competitors, indices and trends are generally the most useful way to provide a quick overview of business, into which viewers can then dig deeper, depending upon their area of interest. You don’t need to show it all on the dashboard, and you shouldn’t try, just keep the summary to the KPI’s that are most relevant for everyone to know.

For more on how to choose your KPI’s see here.

 

#2 Make your information more available

You already have many sources of information, but who has access to it? If you are like most companies, it is the department that collects the data that analyses and uses it, and other departments rarely know of its existence, let alone get to see it. Why not develop a library in which you can store all the information and insights that are gathered and developed, and then give everyone access to it?

This library can be as simple as a folder in a shared file, an Excel folder, an Access database, or a more sophisticated system that can manage budgets, projects and suppliers, as well as the storage of the processes and reports. Some organisations are afraid of doing this for fear of information getting into the h ands of their competitors, but access rights today are easy to manage so that you can significantly reduce such a risk.

You can find more information on knowledge sharing here.

 

#3. Make your information more actionable

Much of the information that companies gather is backward looking, coming from sales and distribution that have already happened or your customers’ consumption and usage habits of last week, last month or last year. Whenever you gather any sort of information, it is a good idea to review the description of your target audience for each br and, in order to ensure it is as complete and as deep as possible. This should not be a once a year exercise, at the time of plan writing, but a continuous process to stay in close contact with your customers’ desires and changing opinions and behavior.

You will almost certainly find that in today’s fast paced world, they have changed quite significantly in some areas. However, even if the current descriptions have not changed substantially, the review of your information should enable you to enrich it further for an even better underst anding. Additionally, in order to build insight and foresight the information you gather needs to be complemented by forward looking metrics such as trends and future scenarios. By looking at how your customers are adapting today, and hypothesizing on their future changes, your organisation will be better prepared for future opportunities and challenges, providing a real competitive advantage.

To learn more about developing your Vision & strategy check here.

 

#4. Make your information more readable

If you have gotten beyond the amount of data that is humanly possible to analyse, you need to consider building a database that can be analysed and modelled with the help of complex analytics. This is when information starts to become BigData and can result in a step-change in the insights an organisation can gain from it.

The sophisticated algorithms that can now be developed can make your information “speak” more clearly about your customer and become usable for many different purposes. You can try hypothesizing about your customers future behaviours, the likely success of your promotions or innovations by region or country, and then get near real-time answers to your questions about them. In some cases, you can even simulate market response to new ideas before they are even launched, in order to identify the best roll-out plan, or even to decide whether or not to launch in the first place.

If you yourself are at this tipping point, as descibed in Malcolm Gladwell’s book of the same name, and need support in developing your integrated marketing database, please contact us so we can share with you some of the successes our clients – your competitors? – have already had.

These are just a few ideas on how to make more and better use of the information you are already gathering. What made the biggest change for your own organisation in the use of the information and knowledge it gathers? Have you reached the tipping point to BigData yet? If you are proud of what you’ve done, why not share it with everyone here?

For more information on developing processes for the integration of information, the development of insights and the internal sharing of  knowledge, please check out our website: https://www.c3centricity.com/home/underst and/

C3Centricity.com uses images from Dreamstime.com and Kozzi.com

Advanced analytics can help marketers know their customers

This week’s guest post comes from Ray Eitel-Porter, Executive Director and Leader – Europe, of Opera Solutions, C³Centricity’s Big Data Analytics partner.

Analytics can help marketers know their customers’ preferences, anticipate their behavior – and take the right steps to influence both.

Today’s advanced analytics allow marketers to detect the signals that indicate how customers will behave – whether positively or negatively – and identify the steps they should take to reinforce the former and head off the latter. It’s a truly customer-centric approach that works across various industries.

Opera Solutions, a leading Big Data analytics company with more than 220 data scientists – one of the largest such groups in private industry – is in the forefront of helping companies use the latest predictive analytics to better underst and their customers. Here are some of the successes we have seen when marketers use advanced analytics to connect with their customers:

A company in the hospitality industry achieved far greater customer centricity by creating a customer record with behavioral tags that explain each individual customer’s reaction to a particular offer and help tailor future ones. By scraping information about specific aspects of the offer – a hotel’s amenities, the reputation of its restaurant, types of nearby attractions – a picture of this customer’s likes and dislikes comes into focus over time. Taking this one step further, by comparing a customer’s behavioral signals to other in-market consumers with similar activity – a “twin” – the company can infer that the customer’s “twin” will respond in the same way. The result: it lets the company serve up timely, relevant offers to a broader, more receptive audience.

An analytics-based approach to customer centricity can also detect the faint signals of a customer that’s about to stop using a business or service – sometimes, even before the customer knows it. Marketers can take early action to reverse this fading, through more individualized interventions. For example, a weight-loss company now gathers as many behavioral indicators as possible on each of their customers – focusing heavily on their website behavior. Then they use them for “survivor analysis” – scoring activity on a daily basis to determine if a customer is at risk of attriting. All this allows them to rapidly identify those individuals in danger of leaving in time for the company to take action.

A food retailer has taken customer centricity to the household level. It looks at the purchasing history of customers on a home-by-home basis, compares what one household buys to what similar ones buy, tracks spending in specific categories over time, and pushes recommendations right to the point of sale. The result: an increase in incremental revenue on the order of $100MM, versus a 16 percent reduction in sales in the control group.

These are just a few examples of the new ways that advanced analytics can drive real results for marketers – and it’s just the tip of the iceberg. Big Data is an incipient gold mine for marketers, containing information, patterns, indicators, and signals that can refine target markets, serve up better recommendations, help optimize prices and offers, and much more. Advanced analytics are the means to extract the gold from the dross – and they are only going to become more powerful at doing so.

For more on C3Centricity’s partner Opera Solutions, check out our website:  https://www.c3centricity.com/about/

 

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