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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Leading Marketing Excellence with Analytics - Visa Case Study

Recently I had the privilege of hearing Nathan Falkenborg, Head of Consulting & Analytics, North Asia at Visa speak at the SAS Executive Forum in Singapore. Nathan has also spoken at SAS Premier Business Leadership Series where he talked about how the analytics guy won over the marketers at Visa.

Nathan immediately captured attention by asking the audience what Marketing Excellence is. I’m not sure what was going through everyone else’s mind, but I was waiting to hear key insights from this marketing leader. I was not disappointed - here’s my top six takeaways from the keynote.

Marketing ExcellenceMarketing objectives
Everything you do in marketing needs to be underpinned by your business objectives. Everything. Simply put, if you’re not tying your results back to the business you’re not leading marketing excellence. If your underlying focus is on the business objectives, and therefore how marketing will achieve these, then you can feel much safer in your analytical marketing journey.

Seek Risk
Marketing excellence starts with the creative marketer who seeks risk. This means you’re thinking of new ideas and always asking yourself “how can we?” and are open to the possibility (probability?) of failing. It’s important to understand that when you’re testing new things, give yourself the opportunity to fail fast! This then becomes part of a test and adjust mindset, rather than throwing your hands in the air and declaring failure.

Be truly customer-centric
In a customer-centric organisation it should be the case that the marketers, the analytics team and the frontline staff all have an excellent understanding of the customer. Usually this is from a variety of different perspectives, like interviews, data and analytics. The customer centric marketer has a deep understanding of the customer through the data – that’s customer intelligence. It’s what you do with that data to drive business results.

Test everything
Develop an experimentation mindset. Above the line measurement is difficult and it takes time to get a common model to measure to understand the ATL activities. It’s tough. Digital advertising is very measurable but doesn’t necessarily connect you to the end result, be it the change in consumer behaviour or the specific change in sales. Every campaign must be considered a test.

Unlimited metrics
The number of likes and impressions don’t mean anything unless they translate into business results. Digital is interesting but you have to connect it to the bottom line. To build your marketing excellence, measure your existing customers, below the line. If you’re not comfortable, start with A/B testing. Sounds easy but it’s not. You need to learn how to measure and how to measure appropriately.

How did Visa do this?
The first step is to design the experiment. Target the right people to optimise the return. In financial, the measure is acquisition, that is, the response in the channel. Did you respond? Could I approve you? Did you become active? After we can answer these questions, we can develop a model based on who you look like then we can understand your predictive lifetime value. To learn more about how Visa uses SAS read this case study.


Looking at how you can develop your marketing excellence? Take the Customer Intelligence Assessment to understand your current marketing capability versus your planned capability. What do the gaps look like?

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Avoiding the pitfalls of multi-channel customer engagement

PitfallIt seems like everyone is searching for ‘best practice’ these days. We are constantly looking to learn from what is being held up as good, leading and perhaps even the best itself. While this is a valid exercise, I believe we are missing an opportunity to take a closer look at ‘bad practice’. That’s when either people, processes or technologies create a scenario in which the business case doesn’t hold true and where the project – or parts of the project – eventually fails.

However, we really should take that opportunity because there are important, valuable and very tangible lessons to take away from most such cases.

1. Avoid approaching a one-to-one customer engagement project as a data integration exercise

Building a data-structure that supports the organisation learning about customers’ interactions, responses and preferences over time is a data integration discipline, but the initial approach needs to avoid looking at it that way. It might be that the organisation has 50 different systems with customer and market data in them, but the incremental value of integrating the last 45 of them might not be very significant and only hold very little competitive advantage. I’ve seen organisations spend 12 to 18 months on data preparation and data design but by the time their communication actually hits the channels, things have almost certainly changed. The customer might have new priorities and competitive forces could have shifted.

So my advice is – spend time thoroughly understanding where the valuable use case and competitive differentiation is and build the data processes, the analytics and the automation to address your highest priority use case. Doing so will get to a business outcome much faster. Moreover, it makes it much easier to ask for additional funding to add new data sources, new channels and grow your model’s maturity.

2. Don’t overlook or underestimate how data-driven customer engagement impacts your current way of working

Tailoring emphasis and investment to an analytical way of going to market is easy in theory but hard in practice. Intelligent, real-time recommendations to point-of-sale systems or call centres are only smart if they are being actioned. Call centre workers are not marketers and the churn rate in such teams is often high. So work with them and ask for their input as to how offers and service messages should be served in order to make their everyday life easier. Ask what they think could create a better customer experience in their customer touchpoints. This will not only refine your requirements, but will also start the much required change-management process at an earlier stage.

Take the same approach when aspiring to analytically optimise customer contacts – right message, at the right time – you know the mantra…Recognise that optimising won’t work at all if the business process is designed in a way that has brand managers or branch executives assigned to groups of leads/customers to market themselves to by the beginning of the month or quarter.

3. Don’t focus on functions and features before balancing them against a solid understanding of the implementation team’s skills and experience

Having been through buying cycles, implementation projects and even business-as-usual states a few times now, it always strikes me how much time and effort an organisation will invest in a near-FBI-style interrogation of functions and features when they are choosing systems to drive their multi-channel customer engagements. I’m not saying functions and features aren’t important, but the weight buyers attach to them needs to be balanced against a thorough understanding of the people and skills their vendors can provide in order to deploy and support the software in a timely and high-quality fashion.

As with my ‘overlooking’ and ‘underestimating’ point above – the days are long gone when marketing was just nice to have and ‘so be it’ if campaigns got delayed a little. If marketing is critical in driving tangible sales and customer experience outcomes then systems selection and implementation require a close relationship with the software vendor/system integration partner. This will ensure the business implements the right functions and features it needs within the right time and of the right quality.

This post first appeared on marketingmag.com.au.

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Science fact: “Model Factory” means getting the basics right.

I press a button, a miracle machine churns through all the calculations in the world and the answer to the Ultimate Question of Life, the Universe, and Everything[1] is produced as a single number. Oh hang on, that’s 42. Alright, for our microcosm, let’s stick to the answer to my customer’s behaviour, my products’ demand, when the widgets of my machines are going to fail or how my customer feels. Too much to ask?

Once upon a time this was science fiction. Not anymore.

As people across all parts of the organisation jump onto the potential of data, we naturally start asking more, bigger and harder questions. And we want the answers now. Minority Report[2] may be in the near future but for today, how do we just keep up with everyday demand and still have time to think of new questions to ask?

We may need our own miracle machine to give us space and time back.

Model FactoryIn practice, we want something that works in the background to analyse data and derive predictions, with little requirement to change or interact, but at the same time is robust and trustworthy so that the results and justifiable and make logical sense. Our machine for producing these predictions, a “model factory”, should automate, accelerate and maintain governance over a series of logical processes across the Analytics Lifecycle from data preparation to exploration to model development to deployment & monitoring.

This comes down to a machine that has well-oiled technology, has clearly defined logic and is up-to-date and maintained. Building and running this machine will take 8 Ps: people, process and product (technology), possibilities and the old saying “preparation prevents poor performance”. Being prepared just means getting the basics right.

Possibilities: look beyond the "safe zone" for goals to strive towards.

  • Engage people (internal and external) who have done it before to develop on ideas and plan a realistic roadmap.
  • Keep an ear out for new trends in process improvements and analytical techniques e.g. conferences, association meetings, publications.
  • Allow dedicated time and/or resources to experiment with existing and new data sources to learn dynamically and determine the next innovation.

People: create a culture to attract and retain the right people to create, maintain, update and interpret the machine.

  • Give people direction and guidelines but room to create and innovate e.g. by providing separate processes and technology environments.
  • Create a team of people with various skill sets – business, domain, technical, unicorns – but who speak a common language and have a common goal.
  • Keep the day job interesting with side projects e.g. enablement, secondment, research.

Process: implement, enforce and reinforce processes which improve productivity and question others.

  • Automate standard reports and make the others, as much as possible, self-service e.g. use interactive visualisation and Microsoft Office add-ins.
  • Give access to the right information to the people who need it e.g. common intranet site, locked down operational vs. dynamic discovery.
  • Document stages of the process in standard templates for reusability and governance.

Technology: match the right technology for the task at hand and leverage modern infrastructure.

  • Project objectives, user skills, time constraints and the format for consumption will dictate the technologies required to solve tasks e.g. exploration, operational, experimentation, integration with front-line interfaces.
  • Provide high-performance technology – machine learning, multi-threaded, in-database, in-memory, template-driven, workload-managed – to accelerate the cogs of the machine.
  • Integrate each stage of the Analytics Lifecycle with common metadata to improve seamlessness.

These basics are the first principles to a big bang “model factory” – everything else will fall into place. This machine is a living entity that will evolve over time as technology advances, analysis trends change and objectives are redirected, and in this way will need to be kept up to date and modern. But by getting the basics right, the machine, at whatever stage of its and your organisation’s evolution, will form the foundation for your future intergalactic purposes.

May your “model factory” not be in a galaxy (too) far, far away.

Learn more about Machine Learning, High-Performance Analytics, Unicorns (the people kind) and try SAS Visual Statistics at the links. For those in Australia and New Zealand, keep an eye out for webinars, Hands-on Workshops and conferences throughout 2015.

[1] The Hitchhiker's Guide to the Galaxy, Douglas Adams (1979)

[2] Minority Report, Twentieth Century Fox Film Corporation and DreamWorks SKG, Dir: Steven Spielberg (2002)

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  • About this blog

    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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