A Shopaholic’s Guide to Analytics II.B

The Rule of Three is a writing principle that suggests that things that come in threes are inherently funnier, more satisfying, or more effective than other numbers of things – Wikipedia.

3 Ps of success, Blind Mice, Little Pigs, Stooges, Musketeers, The Matrix, The Lord of the Rings, rings, pairs of shoes, 3 year memberships… Everything is better in 3s – including this shopaholic series!

  1. A Shopaholic’s Guide to Analytics
  2. A Shopaholic’s Guide to Analytics II.A: Half a shopping bag of useful techniques in Analytics
  3. In this last hoorah on the topic of Retail (therapy) Analytics we’ll empty our bag of some the most useful analytics techniques for keeping our customers happy and loyal. From the customer’s perspective, these are “A feeling that I got a good deal” and “Convenience”.

Today I am referring to any entity that transacts as a customer – individual, household, business etc. and any item for sale or service as a product.

A feeling that I got a good deal – what offers, when and how often?

Whether it’s the word SALE or finding a rare collectable, we all want to feel that we had a fair transaction. But as a retailer or service provider, we need a balance between being competitive and fair to our customers and staying in business.

What is the impact of price on demand?
How long should a promotion run before it’s unprofitable?

shopaholics-guide-to-analytics-1Price does not affect demand of all products the same way – electricity versus floor cushions, burgers versus Porsches. The economics 101 method to understand this impact is price elasticity / sensitivity – the ratio of the percentage change in demand over the percentage change in price. “Elastic” products (ratio greater than 1) are sensitive to price changes.

However, price and demand change over time, sometimes seasonally but not always consistently – affected by economic and other environmental factors. In this case, time series forecasting “causal models” (described in “The right product”, II.A) can be used to model the relationship between price and demand directly. From this model, price elasticity can be calculated, or what-if scenarios can be run to measure the direct impact of price, taking other factors into account, at points in the future.

These techniques can also be used to quantify expected impacts of promotional activity, length and frequency and avoid over promotion.

Which creative is more appealing to customers?
Which product offer is more profitable?

Predictive models and optimisation techniques (described in “Good service”, II.A) can be used to best allocate competing offers or where similar offers have been given in the past. If there is no history, we need to test the effectiveness of our offers through experiments on small samples of customers and extrapolate these to what is likely in real-life. This is known as a choice experiment. To derive statistically viable decisions, we use experimental design to make sure we are capturing sufficient information across the different choices. A simplistic form of this is an A/B test.

Convenience – what is relevant and where?

If shopping was a sport, then as an elite athlete, I expect towels to be stocked in the locker room and the showers to be functioning. Basically, there’s enough going on in our lives – and often too many other competitive options – for customers to deal with difficult or restrictive processes.

Yes, I realise I sound like a brat. But as the e-tailer market grows, people continue to work longer hours and globalisation is a reality, it is even more important for retailers and service providers to make transacting easy. There are operational considerations – integrated systems, web design, accessibility, etc. – but there is also the need for detailed profiling to understand the viability of the target market.

What products are the most relevant?
What is the best store layout and window dressing?
What are the most effective channels?

Demographic – a profile of the different types or segments of customers and how they are likely to behave under various circumstances e.g. during lunch breaks, with young children, in retirement, etc. Using statistical segmentation techniques such as clustering or self-organising maps are useful for creating segments but profiling is the process of differentiating these segments and is done through slicing and dicing and visual exploration[1].shopaholics-guide-to-analytics-2

Where should we build the next store?
Where should we locate the distribution centre?

Geospatial and location – a profile of the geography and terrain overlayed with hotspots of activity e.g. industrial, commercial, residential, thoroughfares etc. and, to optimise decision making, demographics and economics. Geospatial visualisations and network maps are helpful to highlight and differentiate between these areas of interest.

BUT convenience is underpinned by how well we understand our customers’ needs.

Do we have enough of the right products?
Are we proving exceptional customer service?
Is there sufficient value and choice?

I hope that you have picked up a pair or two of comfy shoes to help you on your analytics journey. If you ever feel lost in the sale crowds, as with the sport of shopping, focus on one thing at a time – an “aha” moment you can make a reality. Set your well-articulated goals and invest in the right-fitting solution of people, process and technology for the relationship you want to have with your customers.

Learn more about how you can quickly get started with exploring your data in the cloud with this on-demand video, Insights in Seconds.

Happy shopping!

[1] SAS Enterprise Miner has the out-of-the-box ability to profile segments statistically using comparative graphs.

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Decisions first!

decisions-first-tom-stockI moved to Australia from Belgium two months ago for a short-term assignment. I am very concerned by the exchange rate. My dollars have lost over 15% of their value in euros and I share my frustration around me. People tell me, "Just wait, it cannot stay so low, the rate will go up again". So I keep waiting.

Actually, this intuition is against all economic principles and historic observation. If there was information on the market indicating the value of the dollar is underestimated and will go up again, it would be immediately incorporated in its price. Making the intuitive assumption that the dollar will come back to its historical value is like buying a lottery ticket. You are only buying a dream. It has the exact same likelihood to go up or to go down again.

Yet, I am taking the decision to wait. I don’t need the money now, and I buy the dream that the value of the dollar will rise again, with an eye on the next iPhone I could buy with it.

Many organizations have to take the exact same decision as me.

If you were an executive in a company with Australian dollars in the bank, shareholders in Europe and no local investments in sight, would you wait? Would you consider it a rational decision? Can you afford the same subjectivity as me? Can you buy the dream the value will rise again?

Doing business is taking decisions and companies cannot make subjective decisions. All business decisions must be treated with a maximum level of objectivity incorporating all available information. Whether these decisions are strategic, tactical or operational is irrelevant to this principle. So, organizations cannot buy the dream the dollars will rise again.

And it is a challenge for many organizations to get rid of that subjective bias. They must engineer their processes in order to take optimal decisions, requiring the right information at the right time. This information consists of analytical insights, patterns, sentiments and anomalies hidden somewhere in data. It will require to read, store, crunch and process tons of records, texts, trades, news and databases at extreme speed to meet the decision timeframe.

This process must start by defining the decisions that the company want to objectivize. The price of an airline ticket, the purchase of additional ships for oil prospection or the insurance premium of a policy holder.

These decisions define the insights needed for improvements. It may be simply surfacing the most recent update about a customer. It may require some probabilistic computations of oil prices or sentiment analysis based on previous phone conversations.

And only those expected insights will ensure the organization put up the right data requirements. What data is needed to provide these insights? Comment fields, sonar echoes, phone calls, pictures? Is the data to be stored or only processed? Where and how does it have to be stored? Etc.

Organizations must aim at decreasing the subjectivity of decisions and this process must drive the requirements for technologies. As such, decisions will define the analytical strategy, and analytics will define the data strategy.

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A Shopaholic’s Guide to Analytics II.A

I realized a little while ago that I may have more loyalty cards and memberships than the average person. (And that I more actively prove my loyalty than the average person). But as anybody who has ever signed up to a mailing list or for a store card knows, having a loyalty card doesn’t necessarily guarantee loyalty (unless you think of shopping as a sport). It just means that at one time we were enticed by a “shiny object” or a great consultant that deserves a raise.

There are statistics, statistics and statistics out there on loyalty but here are a few that total the rest:

  • On average, loyal customers are worth up to ten times as much as their first purchase – White House Office of Consumer Affairs
  • The probability of selling to an existing customer is 60 – 70%. The probability of selling to a new prospect is 5-20% – Marketing Metrics
  • It costs six to seven times more to acquire a new customer than retain an existing one – Bain & Company

So yes, at the risk of seeming self-serving, it is worth keeping an avid shopper like myself happy.

In the first of this series I listed the things that would keep most consumers happy. I’ll cover the first two in this article. There are, of course, nuanced complexities across different markets and retail sectors (diamond rings versus ice cream versus wrapping paper), but simply put, customers want a good relationship with their retailers and service providers. If you don’t believe me, refer to the statistics.

Let’s get to the cashier at the end of the aisle – how do we use the data we have about our consumers, products and services and market to be successful? I refer to any entity that transacts as a customer e.g. individual, household, business, etc.

The right product – what product, when and for how long?

We know, even just by observation, that there are products and services that are most appropriate, or in demand, at either certain times of the year, or stage of a person’s life. For exampretailforecastle, don’t try to sell me income insurance when I am about to retire, but feel free to sell me froyo all year long.

Understanding an individual customer’s stage of life is important when targeting them directly (more on this in the next section), but to understand “the right product” to have in stock, the trick is to know how demand changes throughout time. This is called demand sensing – understanding the troughs, spikes and plateaus of demand, throughout a year and over years.

We sense demand by using time series forecasting techniques that break demand down to:

  • Trend – is demand increasing or decreasing on average?
  • Seasonality – does demand generally peak or drop at certain times of the year?
  • Known – do we have data to support a spike or trough like promotions, holidays, economics?
  • Unknown (because we can’t know everything).

The most common time series forecasting techniques are ESM and ARIMA models. ARIMAs have an added advantage over ESMs of being able to take “known” factors (events, holidays and other information that may impact demand). These types of models are sometimes referred to as causal models because they can measure the impact, duration and complex interactions between trend, seasonality and known factors. For example, sweaters need to be discounted by 80% in summer unless recently worn by Taylor Swift on an album cover released within the last 6 months and/or exclusively sold online for 2 days to countries in the other hemisphere.

These models also allow us to understand the impact of different scenarios e.g. what if we run the promotion for 3 weeks instead of 2, what if we increase prices by 10% because of supplier shortage. Applying advanced forecasting techniques like these helped Nestlé Oceania more than halve their forecast bias, streamline their process and better understand the impact of their promotions.

Once we can sense demand we can then start to shape demand by optimizing across the supply chain. For more on demand forecasting follow The Business Forecasting Deal blog or in this title.

Good service - who to target, how to target and why target?

Ok, this is a given. Well-trained, customer-centric staff is precious gold, but knowing what trained staff should offer and when to make the offer is conflict-free customer service diamond.retailcustomer

Some types of data attributes that are useful for predicting the best treatment to give a customer:

  • Transactional – purchases or inbound interactions made by the customer
  • Behavioural – responses by the customer to interactions and patterns in transactions
  • Geodemographic – statistical characteristics of a customer e.g. gender, region of residence
  • Derived 3rd party – data accumulated over various sources providing pre-calculated metrics that can be applied to a customer.

If a customer always shops when they are given an offer (SALE!), they will probably continue that behaviour. However, as we know, people don’t all behave the same way, and trying to get a headline description of our "target customer" isn't easy.

One method to generalize customers is to group them into segments using data attributes. A common technique is Recency, Frequency and Monetary/Value segmentation (RFM). RFM uses data on how recently a customer transacted, how often a customer transacted in a period of time, and how much a customer has spent/cost in a period of time to split customers up across a cross-section of those dimensions e.g. low tenured but high value customers, high value lapsing customers, etc. Keeping customers (like me) that are high R, F and M happy will generally work out well.

If we want to take it to the next step, we need to learn from attributes of customers that have and have not responded to offers and channels in the past, create a picture (model) of how to differentiate them, then extrapolate the model to give us what is likely to occur in the future. This is called propensity modelling, a form of predictive modelling. Using statistically-based techniques rather than business rules, removes the need for us to pre-suppose every detail about every customer. These techniques are used to predict the likelihood of a customer taking up an offer, lapsing from a loyalty program or even the life stage and lifetime value of a customer. For larger data, this is best carried out using machine learning techniques in a data mining framework for automation and validation.

One step further is to optimally allocate an offer amongst competing offers and channels to a customer while accounting for operational constraints (budget, resources) and customer preferences (frequency of contact, other products of interest). New Zealand’s leading coalition loyalty program Fly Buys, uses a combination of these techniques to target customers with the best offers and maximise the return their partners gain from the program.

Finally, the ability to understand what customers are saying in surveys, through the call centre and on social media forums about our products, services and processes adds further power to predictions. Applying text analytics techniques that use a combination of machine learning and linguistic rules to extract sentiment, discover discussion topics and predict outcomes is how organisations like Lenovo decreased the number of call centre requests by 30%-50%. Marrying behaviour with feedback is the fundamental objective of good customer service and the basis of concepts like Net Promoter Score.

Thanks for reading! I’ll be discussing “A feeling that I got a good deal” and “Convenience” next in part in II.B of this blog. Until then, if you want to start an analytics conversation within your organisation, I recommend tuning in to our webinar Insights in Seconds, where we showcase ways to get up and running with our hosted cloud offering and start uncovering insights in your data for yourself.

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A Shopaholic’s Guide to Analytics

If you know me, you know two undeniable things (other than my love for froyo): I consider shopping a sport and I am an Analytics geek. Being an Analytics geek means that I see potential for using data everywhere, and never more than when it’s my data as a customer. And through sheer perseverance – I think of it as contributing to the economy (I’m not an economist) – there’s a lot of it sitting in data stores around the world.

Privacy aside, as far as I, the customer, am concerned, the data is there so that I can have a better experience, and so that my retailers and service providers can make (just) enough money to keep improving my experience.

As a customer, what do I want?shopper

  1. The right product – don’t you hate not being able to find the thing you have psychologically and financially prepared yourself to buy?
  2. Good service – I want to be a (very) repeat customer so give me a reason!
  3. A feeling that I got a good deal – at least in my head…
  4. Convenience – with work, friends and family, I am all about efficiency.

Turning the (credit) card over, for a retailer or service provider what do these four points mean?

The right product – do we know how demand for our products will change?

Do we know our peaks and troughs in demand to make sure that shelves are never bare without the right products and that our storage space is not wasted on too much excess stock? There are few monopolies now in retail and so as a customer it’s much easier for me to spend my money somewhere else in town or somewhere else in the world online. And if I have to go out of my way to find a particular product, I am (most) likely to buy other things while there.

bottlesIs demand consistent like cooking chocolate that spikes 6-8 weeks before Christmas, or ice cream that is driven by changes in the weather? Or is it irregular like a fashion item that comes in and out of popularity based on advertising investment, celebrity endorsement or what happens on the Paris catwalks?

What are our product demand trends and what are the things that impact them?

Good service – what can we do to attract our customers back?

If you want loyalty, the shopping experience needs to be emotional. Other than pleasant and helpful staff that appear magically at the exact time they’re needed, what services suit the personality of our products and expectations of our target customers? It may seem counter-intuitive but there is a café in my city that is known for its less than nice staff to match its moniker – but it’s always packed, probably because of its wide selection of cakes, and inner city location near a university.

happyWhat are our customers saying in surveys, verbally and on social media? Are they checking in with positive or negative comments? Do they like our décor or do they want more cake options? Once we know who our target audience is and how they think, we need to ask where are our target areas to setup shop and what products should we stock up on? Is there a group of customers that are high value and need more attention?

How do our customers actually feel about us and what can we do to keep improving?

A feeling that I got a good deal – how often do we need to run promotions?

Let’s not beat about the bush, when I see “SALE” I have an instinctive need to enter into the uncomfortably crowded store and generally feel obliged to make a purchase. But I, and my bank, wouldn’t necessarily classify me as a bargain shopper. So what is the right amount I should be promoted to – both to retain my loyalty as a customer but stay profitable as a retailer?

offerWhat is our point of diminishing returns for promotions – is “more” better, or is “more” just more? Does buying a hot chocolate mix cannibalise on instant coffee? Could promoting a cheaper red wine still lead to the purchase of a more expensive red wine with the cheaper wine? Is “30% off” more attractive than “Buy 2 get 1 free”? To really know, these variations need to be compared and tested. For a small number of products, knowing our customers and products could be enough but where there are unknowns, we need analytics to help provide those answers.

Do we know the price tolerance of our target customers for our products and the maximum quantity cap per customer?

Convenience – are we using the right channels for our customer?

Some may say that convenience is just another word for lazy, but with so many competing activities, convenience is a necessity. Like when I discovered online shopping (sighs). For me, it’s just another channel for my sport, though a channel that sometimes disappoints because I am a tactile shopper – sitting behind a computer is just not as much fun. But it’s so convenient (and dangerous).

mobileWhat are our customers expecting – what makes sense for our products, environment or culture? Which of our products make sense to have in-store, online, sold through consignment or some combination? Do we need apps for online purchases? Should these apps have embedded payment methods? Should we include free shipping (yes!) or same-day delivery? Does everybody deserve this service or should it be exclusive?

How can we get our product closer to our customers in the most cost effective way?

So many questions and so little time, where do we start?

It's not immediately with the data, though there is a lot of this and analysing it is important.

  1. First step is to ask: what is our ultimate goal – is it customer satisfaction, profit, exclusivity, humaneness…?
  2. Ask questions from our customer's perspective - use our personal experiences if we are the target market - and map it to our goal. We need smart people - domain, objective and subjective experience and systems knowledge - to ask these questions. There are no wrong questions but as we learn new things from the data we didn’t previously know, we will find better questions to ask.
  3. Anything we don’t know straight away – demand shape, ideal customers, regional profiles, customer sentiment, price sensitivity, optimal contact points, economic breakdown – we can almost definitely find out through the data available internally (price, promotions, supplier, POS transactions, call centre) or externally (competitive, industry and economic trends, social media, disruptive strategies).
  4. Most importantly, be open to the results – every surprise is a free gift from our data.

Stay tuned to the next instalment for more on how to get the right answers to our questions. If shopping was a sport (pretty sure it is), it would be a marathon rather than a sprint, just as analytics is in retail – we must start somewhere, some is always better than none, and with a clear goal and the right support, we can build on smaller precious victories and become a champion.

For avid shoppers, retailers and service providers, start getting competitive offers at the SAS Retail Analytics store of information. Eager to learn more and get hands on with a retail-specific scenarios? Visit the SAS Visual Analytics Try-Before-You-Buy website. Scenarios include Customer Analysis and Promotion Effectiveness.

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Transparency is the new currency in marketing

The boundaries between the company and its marketplace are increasingly blurred. We are now part of a reality in which customers play a much more integrated and active part in the processes of research and development, marketing and customer service. Little about a company and its offerings will ever again be invisible to the marketplace.

Marketing departments must transform themselves into facilitating units that create a foundation for dialogue with customers. This puts tremendous pressure on an organisation’s accessibility, integrity and honesty – not only in respect of damage control and the management of critical situations – but to become a new and highly transparent source of sustainable competitive advantage.

Working with customer centric organisations around the world, we have seen that addressing these blurred boundaries and the transformation this calls for identifies three main competitive advantage imperatives to consider, depending on the industry you are in:

  1. Employing customer intelligence to create an understanding of the customer journey across channels
    Customers expect to be recognised across channels and a key requirement for any relationship building is to understand the complete cross-channel journey. Only by gaining that level of understanding will you be able to give your customers the best overall experience throughout the lifecycle of their relationships with you. In general, customers are perfectly willing to exchange their information with a commercial organisation provided they enjoy something in return. This means better and more relevant offers, improved service and more personalised attention. Customers understand and accept the logic of exchanging information but is your organisation ready for the part it must play?
  1. Allowing customers to leverage your intelligence capabilities to make more informed decisions for themselves
    Examples could be: telecommunications customers exploring and visualising historical call and network data to understand what plans are best suited for their individual needs; or banking customers analysing historical financial transactions to figure out trends and their preferred investment product options. If your organisation is serious about competing on long term value creation, this transparency shouldn’t be a scary. In fact, with today’s increasing hunger for digital self-service, such initiatives are more likely to create a valuable differentiating factor.
  1. Creating new revenue streams based on your customer and market insights
    What if your customer and marketing intelligence became so rich and granular that it could actually offer value to other organisations? Could you create new revenue streams based on your customer data and your direct marketing platform? For example, telecommunications carriers around world are realising that the data and reach they have are invaluable and would be the envy of any retail marketer starting to build new business models and extend current ones. Think also of media companies and what they know about trending topics and the diffusion of information, both your own and generally. Then ask what other businesses could use that information and enjoy the reach of a publishing company; within the boundaries of appropriate privacy, of course.

There are very many ways to take advantage of new opportunities in customer and marketing intelligence to break down the boundaries, increase transparency, ensure improved customer experiences and exploit potential new revenue streams for competitive edge. And I haven’t even touched on the value of greater transparency for when things go wrong and organisations need flexibility and the ability to implement actions for fast recovery. That’s a whole additional angle to the marketing intelligence story.


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Are you missing out when it comes to data monetization?

After doing some recent research with IDC®, I got to thinking again about the reasons that organizations of all sizes in all industries are so slow at adopting analytics as part of their ‘business as usual’ operations.

While I have no hard statistics on who is and who isn’t adopting analytics, the research shows that organizations that do leverage analytics are more successful on average than those that don’t. What we need is a new analytics experience, an experience where organizations can:IDC_140029_Monetization.600px_ART_v3.1

  • Make confident decisions
  • Analyze all their data where it exists
  • Seize new opportunities with analytics
  • Remove restrictions for data scientists

IDC states that “50.6% of Asia Pacific enterprises want to monetize their data in the next 18 months”. Are you one of them or are you going to let your competition get the jump on you?

Big data (or more specifically how to actually gain some sort of competitive advantage from it) is top of mind for forward-looking businesses.

Our research with IDC gives us a few clues on where to head when it comes to the monetization discussion.

In the recent Monetizing Your Data infographic (PDF) created by IDC and SAS, three key approaches to monetizing big data emerged:

  1. Data decisioning, where insights derived from big data can be used to enhance business processes;
  2. Data products, where new innovative data products can be created and sold;
  3. Data partnerships, where organizations sell or share core analytics capabilities with partners.

Organizations that adopt and combine all three key approaches to leverage analytics are twice as likely to outperform their peers1.

If you’re looking to truly create value from the stores of data you have then you need to look at deploying analytics.

monetizing-your-data-info-pdf-button                 big-data-resource-ctr-button

1 IDC APEJ Big Data MaturityScape Benchmark Survey 2014 (n=1255) IDC APEJ Big Data Pulse 2014 (n = 854)

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Redefining the scope of marketing to operationalise customer centricity

My previous blog posts here for marketing have centred on data-driven marketing themes and how marketers can exploit data and analytics to create a more customer-centric, fact-based culture. And by extension, how this, combined with quality execution is likely to lead to better customer experiences and improved customer equity.

This time I’m asking the question: Where is this leading us – what is the future promise for marketers?

Implementing the changes I have described above isn’t at all easy because marketing as we have long understood it is changing right in front of us and literally on a day-to-day basis. Small wonder then that some marketers are confused as they struggle to get a holistic view of exactly what they should be doing and how they should be doing it.

Let’s start by elevating the perspective and looking at the common denominators across this rapidly changing environment. Let’s ask ourselves how we, as professional marketers, should go about our job differently to establish ourselves as the epicentre of any customer centric organisation.

It is a fact of life – and a very important and welcome one, too – that concern for customer centricity has moved into the board room. In the main, today’s executive teams understand that customers are the most important asset to any organisation, period. Nevertheless, I am finding that the C-level is still struggling to operationalise customer centricity into the established version of the organisational design and hierarchy.

Marketing, sales and customer service – and even accounts and other parts of the organisation – all have important customer communication responsibilities which they are striving to manage effectively at the various touch points they are responsible for in isolation. But any business-to-consumer organisation that wants to compete successfully in the world of the empowered consumer needs to effectively manage the end-to-end experience throughout the customer journey.

So the question the CEO is looking to answer is, ”Which part of the organisation do I hold ultimately responsible for ensuring that all parts of the value chain are driving towards better customer interactions? Who, exactly, is the one customer centric steward who should be actually looking after the voice of the customer?”

I would reason that the modern CMO is in the best position and is the obvious executive to take on that responsibility. Here is why I say that:

  • Most marketing organisations have started enabling themselves to integrate and drive insights from customer and market data – they have laid the initial groundwork;
  • marketing organisations have been through first generation multi-channel campaign management projects and are starting to understand what it takes to optimise cross-channel customer experiences, and;
  • data and analytical talent will continue to be thin on the ground until we get serious about filling the skills gap but more and more positions like head of customer Intelligence, marketing performance manager, marketing analyst and marketing technology manager have started to emerge under the CMO position.

My take is that marketing must assume the full responsibility for the customer. We marketers must establish ourselves in the position where we are the ones that ensure that every other part of the organisation is able to listen to the voice of the customer and learn from each customer interaction.

We yet have a way to go to get to that position, of course, because we are still fighting the traditional perception that the marketing department is where they create ‘funny posters’. But if we can achieve the customer centricity stewardship I know is the right role for marketing professionals, then the future of marketing is bright and promising – and the CMO will secure a seat right next to the CEO.

This post first appeared on marketingmag.com.au.


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All customer intelligence must be woven into CRM programs – online and offline

customer-intelligenceYou've probably heard many times about the fantastic untapped potential of combining online and offline customer data. But relax, I’m going to cut out the fluff and address this matter in a way that makes the idea plausible and its objectives achievable. The reality is that while much has been written about the benefits of online customer intelligence, it far outweighs what’s happening in most organisations today. In fact, considering how beneficial tapping the data can be, I don’t think enough has been written about what types of online customer behaviours should be tracked and how they could be used to create a better customer experience across all touch points.

So where do you begin? 

It all starts with what you have decided are the objectives for your digital presence – are they to register, to make a transaction, sign up for a newsletter, interact with a certain content object such as internal or third party? Those are generally the key objectives I see organisations having in order to understand the customer journey leading up to these events, as well as tracking and ‘remembering’ when the customer interacts with all the organisation’s available channels to the market. A key aspect is to monitor and understand how external campaigns, in-site promotions and search contribute towards those goals and how this breaks down into behavioural segments/profiles.

Recognising a customer

The next important consideration is – how do we recognise visitors/customers we should know from previous interactions even if they haven’t identified themselves on this occasion? Identification doesn’t have to be dependent on a log-in. It could be through an email address we can match with a satisfactory level of confidence, or it could be a tracking code coming from another digital channel where customers had earlier identified themselves. It’s of much greater value if we can match their behaviour as unknown visitors when the identify themselves and not have to start building our knowledge from scratch at the time of identification.

This leads to the point where we need to explore our options for weaving a visitors’ online behaviours into our offline knowledge about them and how – at the enterprise level – we can best exploit the capabilities of our broader data-driven marketing eco-system. We should ask ourselves, is it valuable to us to be able to send a follow up email to the ones that abandoned a specific form? Can our call centre colleagues enrich their conversations by knowing which customers downloaded particular content? How important is it to us as an organisation to be able to analyse text from in-site searches and combine it with insights driven of complaint data from our CRM system? What are the attributes of the various parts of the journey leading up to completing an objective?

Perhaps you wonder what I mean by the capabilities of the ‘broader data-driven marketing eco-system’. Well, my point is that it that it puzzles me that most organisations today can’t integrate/report/visualise online customer intelligence in the systems that already comprise the backbone of their information infrastructure. They don’t utilise their existing campaign management systems to make decisions on what’s relevant for the individual and drive online personalisation which increase the online conversion rates, but at the same time can be used across channels. Organisations rarely take ownership of online customer data or use their advanced analytical engines and existing analytical skills to drive next level insights.

Not taking full advantage of campaign management systems already in place is opportunity missed because the deliverables of integrated online and offline customer intelligence are very real. We should be looking for them every day.

This post first appeared on marketingmag.com.au.

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3 reasons why focusing on software quality makes business sense

Have you heard of Meskimen’s Law? It states the following: “There’s never time to do it right, but there’s always time to do it over.” If you work in software development you’ve probably come across colleagues who seem too ready to apply this law in the realm of software quality.

Meskimen’s Law is of course meant to be tongue-in-cheek, but sacrificing software quality for more functionality or faster development is no laughing matter when you care about the bottom line. Back in 2002 a study commissioned by the National Institute of Standards and Technology (NIST) in the US found that between 50 and 75 percent of development funds are consumed by software developers identifying and correcting defects, and that software defects cost the U.S. economy almost $60 billion annually.

The upshot is that by focusing on quality a software company can significantly reduce costs and boost profitability. Here are three other reasons why attention to quality makes business sense:

Focusing on software quality promotes business growth

When software is made properly from the outset free from defects, it’s much easier to scale and adapt as business needs change. If a software project is successful and delivers good value to the organisation, there is a better chance the project will require additional features and functionality and be deployed in different areas.

Consider what Executive General Manager for Group Security and Chief security officer John Geurts had to say about Commonwealth Bank’s requirements when investing in software for fraud detection:

“We were […] looking to achieve an economy of scale, reducing data storage costs, enabling reuse across the group. In addition, we needed the flexibility to add new products, services and channels to the platform at a far lower incremental cost than installing another customized fraud detection system.”

Read the full story of how the Commonwealth Bank saw a 95% increase in check fraud detection efficiency.

Software makers who produce quality software thus have greater opportunities to cross sell and to sell into new markets. But if adapting or re-purposing software becomes too expensive or time consuming due to poor original implementation, these opportunities for additional sales are less likely to materialise.

Focusing on software quality promotes customer loyalty

When a company consistently delivers quality products to its customers, those customers tend to keep returning for more. They also give positive referrals to others. Thanks to the proliferation of social media sites such as Twitter and Facebook, the impact of these positive referrals can be far-reaching. So too can the impact of negative referrals.

Companies that use software for high-stakes activities such as fraud detection or credit risk modelling can potentially incur millions of dollars in damages due to glitches resulting from poor quality software. You can be sure that when something like that happens it doesn’t take long for the rest of the marketplace to find out about it. By focusing on quality software, companies can ensure their customers remain happy and tell their colleagues about it.

Focusing on software quality increases brand equity

The value of a company’s brand is derived in large part from customer experience of its products and services. Brand equity is difficult to measure but it can impact the ability to raise capital, hire top quality employees, and charge a premium. Software quality problems can significantly affect the experiences customers have with a brand and the damage builds up over time. Is your idea of a good time spending the morning on the phone with Tech Support talking about error logs? (No offense to Tech Support teams!) A good solid brand with a reputation for high quality is a powerful driver of business growth.

For more on the Quality Imperative and SAS' commitment to product and service quality and customer satisfaction, download this free technical paper (no registration required).

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Turning marketing automation data around to drive innovation

marketing-automationWhen the executives in an organisation start evaluating whether or not they should embark on a marketing automation journey, they are obviously going to ask themselves what return they should expect from doing so.

Likely to be factored in to the evaluation process are obvious drivers such as reduced acquisition costs, improved conversion rates, better net promoter scores, faster campaign cycle times and the scalability of the campaigns that might be launched.

These are all very important value drivers, of course, and depending on the organisation, executive thinking might also need to be supplemented with considerations about staffing, infrastructure, the competition and much more. But I wonder how many executives take a moment to also think about the ‘data-driven innovation’ opportunity that marketing automation offers them.

Marketing automation is in some instances also referred to as ‘closed loop marketing’ and that’s a term that I suggest puts an important additional perspective to the evaluation. After all, one of the key points of marketing automation is that customer contacts and interactions are tracked. Working this way gives the organisation a window on exactly what was communicated with the customer, when and through which channels, and with a record of feedback that also includes the potential outcomes.

The value of such tracking, whether it’s related to service, compliance, marketing as such or anything else is that the logged interactions are stored and can be recovered and newly personalised for future re-use.

The ability to close this feedback loop in a structured and efficient way holds the potential to create a very important source of competitive advantage by nurturing a customer-driven innovation mentality throughout the organisation. By managing feedback structurally over time, the organisation is establishing and growing an innovative commercial environment that can be described as being in ‘beta stage’ at all times. It is creating an organisational capability that allows it to tap into what is effectively an ‘always on’, always updated and non-biased focus group.

Take a moment to think about this and ask yourself, ‘Just how valuable is that?’

I’ll grant you that the answer might not be as obviously quantifiable as the answer to questions such as, ‘What’s the monetary value of improving our call centre conversion rates by 5%?’ or ‘What’s going to be the bottom line impact of scaling to as many as 40 campaigns a month instead of only four, currently?’ But that doesn’t mean it’s not well worth considering. The faster and more tightly knit an organisation can create that feedback process, the more this element of a marketing automation – or closed loop marketing – project becomes a key source of sustainable advantage.

This post first appeared on marketingmag.com.au.


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    The Asia Pacific region provides a unique set of challenges (ความท้าทาย, cabaran) and opportunities (peluang, 机会). Our diverse culture, rapid technology adoption and positive market has our region poised for great things. One thing we have in common with the rest of the world is the need to be globally competitive while staying locally relevant. On this blog, our key regional thought leaders provide an Asia Pacific perspective on doing business, using analytics to be more effective, and life left of the date line.
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