Knowledge Bench – Practical applications of AI

Knowledge Bench – Practical applications of AI

Thank you to everyone who came along to our latest Knowledge Bench event on Wednesday 24 May. We enjoyed drinks, canapés and chat with a whole host of Data and Marketing Technology experts as well as having some cognitive fun with Watson!

Our focus for the event was AI and Cognitive computing, exploring what it means for the market as well as taking a look at some practical applications of machine learning in action.

We welcomed speakers Derick Wiesner from IBM and David Fearne from Arrow to discuss the topic alongside our experts and heard about some truly innovative projects that are pushing new boundaries in machine learning.

David Fearne, Technical Director at Arrow ECS, gave a fascinating insight into his ground-breaking project, How Happy is London?, a live demonstration of large scale data analytics that has recently seen him win the Software and Services category at the Data 50 Awards.

David also gave us sneak peak at other innovative AI projects underway at Arrow, including a brand new project to see if twitter can predict the general election and an exciting charity initiative based on cognitive computing that is designed to help the most vulnerable in society. He left everyone feeling inspired and opened up a new world of AI possibilities!

We were also lucky enough to hear from Derick Wiesner, IBM Commerce and Digital Marketing Agencies Segment Leader, Europe, who talked through some fascinating real world examples of Watson in action.

What was really clear from all our speakers was that AI is here and is already being used in so many ways to help businesses. As David showed us, ‘machine learning is now part of daily life’.


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To find out more about how you might be able to benefit from the latest AI and cognitive developments speak to one of our experts at Bench.

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Amplify showed that Watson is out of the lab

Amplify showed that Watson is out of the lab

I’m back from IBM Amplify 2017 with renewed enthusiasm for Watson Customer Engagement. I left Las Vegas with a sense of purpose; feeling IBM has real direction with A.I and cognitive computing in Watson. Its nailed its transition from on-premise to SaaS and its proposition is strong and clear. Watson is out of the lab and into the marketplace, ready to be discovered…

A.I and Cognitive is left, right and centre     

There are many compelling reasons to use Watson but what clearly came out of out Amplify last week was that A.I and cognitive is left, right and centre for IBM.

I saw some really dynamic presentations from Harriet Green, Richard Hearn and Will Smith, who all spoke fervently about redefining customer engagement in the cognitive era and the opportunities that Watson will bring to connect with customers as individuals.

Ginni Rometty’s address also convincingly set out how bringing cognitive capabilities together with the cloud will enable new innovation to solve problems and create new marketing solutions.

The new wave of people at IBM has led to a change in culture, with those at the heart of the organisation driving a real understanding of A.I and cognitive.

Watson heralds a new era     

With IBM Watson Customer Engagement, cognitive is now accessible through its simplified product range and easy to understand language.

There’s a new clarity with Watson. Its highly structured and well-defined platform, its user-centric design, smooth integration and cognitive expertise is just waiting to be discovered.

The challenge now is for businesses to understand how A.I and cognitive can be practically applied.

Grant Williams 


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It’s time to get practical with A.I and cognitive computing

It’s time to get practical with AI and cognitive computing

Everyone is excited about A.I and cognitive computing – the boundless opportunities, the raft of applications in development and the promise of a new era ahead for marketers. I’m excited too, especially by how these technologies are already being used, but I see many businesses falling into a trap. In the rush to embrace A.I and cognitive computing, many of the practicalities around its implementation are being overlooked.

Truly understanding the technology

As I wrote about last week in the Huffington Post  a large part of harnessing the opportunities cognitive computing and A.I can bring is in truly understanding how these technologies work and how they can benefit an organisation. There’s still a lot of confusion around this.

Many organisations mix up predictive systems and cognitive systems, for example. Predictive marketing is based on analysing huge amounts of data and automating responses. True cognitive computing is teaching a system to think like a person and learn as you train it. It can take data (which does not have to be personal) and learn from this. This, in conjunction with A.I technology opens up a huge range of new ways to reach and interact with customers.

Importantly, although cognitive computing is designed to learn and run independently, it will always work best in partnership with people. For example, cognitive technology can run automated tasks such as reporting or email campaigns, freeing up people to focus on creativity and delivering better customer experiences, such as Augmented Intelligence.

Don’t be seduced by gimmicks 

Whilst organisations are keen to stay one step ahead of their competitors, they do need to look beyond a ‘gimmick-led’ application of these technologies and instead investigate how it can be applied to actively improve personalised customer experience.

To do this, organisations need to step back and start with the customer. Understand how customers are interacting with a brand and what kind of experience they are looking for. People don’t necessarily want a relationship with a brand, they just want a good experience.

The North Face is one example of where cognitive computing is being practically applied to deliver this kind of experience. Users visiting The North Face website can have a similar experience online as in-store, thanks to intelligent natural language processing technology that helps customers choose a jacket by asking a series of questions and learning from the answers supplied. Powered by IBM Watson cognitive computing technology together with Fluid XPS the retailer can provide customers with outerwear suggestions tailored to their needs, creating a more engaging, relevant and personalised shopping experience.

Getting your house in order

Perhaps more fundamentally though, businesses first need to get their own houses in order before embarking on implementing new technologies such as cognitive or A.I.

Innovating and pushing the boundaries of what is possible through the use of exciting technologies is of course great. However, in order to gain value from groundbreaking technology and turn it in to something that will deliver significant improvement to their customers, it is vital that organisations strike the right balance. As Kevin Kelly, author and founder of Wired famously said “perfect what you know”.


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Taking steps towards cognitive computing

The concept of cognitive computing and A.I has been much discussed recently, in the same way that real time marketing was a few years ago. While there have been a limited number of practical applications of this technology to date, there is no doubt that the concept is set to dominate the landscape for some time. All the big players such as Adobe, Salesforce and IBM are vying to take the lead here, with IBM’s Watson in particular making waves in the industry.

The next few years will see organisations start to get to grips with what cognitive computing can offer. While there is much fascination with the potential for cognitive, there is still an element of nervousness from many organisations, especially when it comes to A.I. This is not unfounded, as A.I has not yet reached the point where it can run without careful human monitoring.

There are still fundamentals to be worked out to achieve true machine learning where the machine is fully responding and recalculating on changing inputs without any programming from a human party.

More fundamentally, though, businesses need to look beyond a ‘gimmick-led’ application of these technologies and instead investigate how it can be applied to actively improve personalised customer experience.

For example, this could take the form of a holiday company knowing that an individual likes to ski, has two children aged six and nine, has been on skiing holidays before in the February half term and favours Italian resorts over French ones, and then drawing information from 1st, 2nd and 3rd party data as well as analysing weather statistics and flight information and then offering appropriate holiday options based on this information.

Again, data is the key here. The more relevant data that is gathered, the more personalised the experience for customers. The importance of having excellent processes in place to capture and manage data is perhaps more significant than ever. As data scientist Bradley Voytek famously said while at Uber: “I don’t need to know everything about everybody. I just need to know a little bit about a whole bunch of people.”

Those that succeed will be the ones who can properly leverage both data and technology to make customers’ lives better.


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Real time gets real…

While there was a huge amount of noise about real-time decision-making and real-time next best action marketing a few years ago, we haven’t as yet seen significant practical application of this technology.

This is set to change from 2017 onward. Many organisations looked into or acquired technology to facilitate real-time when it first emerged as a leading trend, but it is only now that many are actually practically applying it.

The reasons for this are manifold. The tendency is often to purchase a particular piece of technology, without first stepping back and putting together a clear business case and roadmap for the technology. Before purchasing any technology, organisations need to address the following:

  • Why are we doing this? Is there a clear articulation of the existing or soon to be business problem we need to solve?
  • What value is the purchase directly linked to? g. saving money, making money, enhancing the brand
  • What is the measure of success? This is often too generic and not specific enough. It should be clearly articulated and documented.
  • What do we actually need from the technology?
  • Will value be achieved through core functionality or through the use of advanced features?
  • Has this been documented in the business and investment plan?
  • Do we have the right operating model and skills to successfully implement and integrate the technology?
  • Are we already utilising the technology we have to best effect?

All too often these questions are not asked or answered until after a piece of technology has been purchased, which can lead to a significant disconnect between what a business thinks it is getting from a vendor and what it actually needs.

This has been the case with many implementations of real time marketing technology, which is why it has taken some time to see significant practical applications of this.


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It was all about Watson at the Bellagio, Las Vegas

Returning from the whirlwind of IBM’s Cognitive Engagement Sales Academy, it wasn’t the bright lights of Las Vegas or the sumptuous hospitality laid on at the many evening receptions that really impressed me but IBM’s focus on partners and the opportunities that Watson promises in 2017.

A new era with Watson  

Sitting alongside IBMers last week at the Cognitive Engagement Sales Academy was quite something; as the IBM Sales team and Business Partners were collectively told the benefits of IBM Commerce becoming IBM Watson Customer Engagement.

There’s been huge anticipation and excitement about Watson since IBM Marketing Solutions was re-branded in November last year to Watson Marketing, and I was eager to attend the Sales Academy in January to really get under the skin of the new offering.

But it wasn’t just the innovation and enhancements of Watson that were so exciting to find out about, it was the whole nature and focus of the conference which makes 2017 feel like the start of a new era in marketing solutions for IBM. This really was business partnership at its best with IBM Sales and Business Partners accessing the promise of Watson together.

What will Watson Marketing bring?   

So what did I learn? There seems to be a new clarity with IBM Watson Customer Engagement, with a highly structured and well-defined platform, distinguished by user-centric design, smooth integration and most importantly cognitive expertise. There are already embedded cognitive capabilities in the new offering such as struggle detection for websites and content tagging, with many more features on the way.

At heart, Watson Customer Engagement has been designed to help overcome the data challenges that marketers grapple with on a daily basis and to manage the growing complexities of big and dark data which will dominate the year ahead. So I left the dazzle of Las Vegas for a snowbound UK really rather excited about getting home, despite the weather, and seizing the opportunities Watson offers in 2017.

Written by Adele Ross.


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Slow down! Is regulation putting the brakes on the hottest technology?

A few weeks ago I was asked to participate in a panel discussion in front of an audience of data experts, software vendors, agencies and customer organisations.  The subject was artificial intelligence and cognitive technology.  It was a lively discussion, ranging from the changes in working conditions brought about by the Industrial Revolution to audience views on the best ways to convince companies to adopt new technologies.

One particularly thorny question was posed; how, given increasing data privacy regulation such as GDPR, should we work with cognitive technologies which use sophisticated and often byzantine algorithms to make decisions for our customers?  GDPR has itself been the subject of this column and should, rightly, be consuming the thoughts of organisations with customers and employees.  However, when cognitive technologies are employed to assist a customer’s journey how will your organisation respond if it is asked to explain a decision?

Read previous GDPR blog here

One of the specific provisions of GDPR is a “right to explanation.”   However, machine learning solutions produce results, in part, by ascribing more weight to certain factors and then making calculations across large datasets using mathematics that even the technology’s vendor will struggle to explain.

GDPR also calls for organisations to prevent any form of discrimination based on personal characteristics like race, gender or health history.  We may assume that cognitive software won’t be delivered with discriminatory factors baked in – but can you be sure that familiar or unconscious biases won’t emerge in the technology?  After all, these are learning technologies that require guidance from human teachers.

Spotting these outcomes behind an arcane technology solution will be very hard but I suspect that will not be a well-received defence.

Essentially what we are seeing articulated here are some of the reasons that these technologies are not pervasive yet – in spite of the ambitions and considerable marketing budgets of the technology developers.  Companies have legitimate concerns about the use of these new technologies and the veil of mathematical impartiality does not sit comfortably with organisations seeking to enhance their customer experience.

Our good old friend, Governance, is critical in this arena.  Understanding what data you have and what decisions are being made using that data will be key to ensuring that the undoubted benefits of cognitive technologies do not create more trouble than they are worth.  It might be less sexy than the promise of the technology itself but the responsibility to do the right thing by your customers – or at least explain your decision – is paramount.

More than just semantics!

At the same panel event another interesting question was posed;

How should we best sell the benefits of AI and cognitive solutions to our own companies?  My response was a cautionary tale but, I hope, offers some insight on how to propose the adoption of these technologies.

An early encounter with the CIO of a household name company left a lasting impression on me as we attempted to position a cognitive and AI solution.  It turned out that the CIO had a PhD in the very subject!  We spent 80% of the initial meeting discussing his strongly held view that cognition means thinking and that, unless I was proposing the single most significant development in the history of mankind, my solution would not be thinking.  The remainder of the meeting was used to explore his contention that, similarly, current AI is not in fact intelligence in any meaningful way but that he was willing to accept that brute force computation might produce some useful insights.  Moreover, machine learning does not mean, apparently, learning in any traditional sense.

The second meeting was with senior marketing professionals and we had certainly learnt our lesson.  That discussion was far more fruitful as we discussed business challenges and how the technology solution’s capabilities might help overcome them.  The label still said cognitive but the tenor of the discussion was a more traditional one!

Perhaps the lessons are very old:  know your audience; focus on your organisation’s challenges and the value of the solution.  Either way, please talk to us and let’s share ideas.

Written by Dominic Bridgman

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Time is of the essence

Time is a very important, and often overlooked, factor in modern marketing. If you were to analyse the send times of major marketing efforts in the retail, gaming and financial services sectors, you would notice a shift to Friday afternoons for the majority of targeted comms. This is naturally explained by the fact that segmentation decisioning and creative development are worked on during office hours, but will your customers understand, or even care? When you think about what you know about your customers, traditional data clusters around:

  • Behavioural/attitudinal – e.g. age, gender, marital status
  • Transactional – e.g. what products they have bought
  • Analytical – e.g. statistical models, web tracking data

But in the world of modern targeted CRM (both inbound and outbound), another important cluster exists: Temporal – e.g. email open/click time, inbound channel visit time The timing of when a customer performed an action is at least as important as what the action was. For example, a customer who reads the majority of their emails on the way into work will have to scroll a way down their inbox to find your interaction if your email to them is 12-15 hours old at that time. Taking outbound in isolation for a moment, segmenting your customers by temporal factors could allow you to create segments such as this simplistic open time example:

  • Morning email open – open majority of emails 12pm
  • Afternoon email open – open majority of emails 12pm – 6pm
  • Evening email open – open majority of emails after 6pm
  • Push – send time and open time close together, so send at any time

The last case there is important, as many sophisticated smartphone users will open emails any time they are awake, but still important to take into account earliest open times etc. Once you add in click times into the mix, you can build out something very sophisticated, and broadcast emails to each segment at an appropriate time, guaranteeing you a higher slot in the customer’s inbox, giving you a measurably higher response rate to a campaign. While you could do this in any data segmentation tool given access to the data, this functionality is actually automated out-of-the-box in IBM Marketing Cloud (formerly Silverpop) in the form of Send Time Optimisation (STO). It’s an incredibly powerful capability operated by a single switch, allowing you to drip-feed temporally optimised communications into inboxes over a 24 hour or 7-day period. When you take inbound into account, a key factor to consider in Temporal Personalisation is purchase intent. Site visitors (either a prospect or an existing customer) purchase intent will vary considerably by time of day and day of week. Will a site visitor at 11pm be looking to make a purchase there and then? Unlikely. Does a site visitor looking at the site at work have the same intent to purchase as they do in the evening, or weekend? With this in mind we can tailor the type and value of marketing messages presented to customers based on these factors, and when combined with the behavioural, the transactional and the analytical, a formidable offering will begin to emerge. And if you factor in geo-locational capabilities, like location and weather, the real time targeting capabilities will start to border on precognitive…

by Tim Biddiscombe


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The rise of the digital personal assistant…..

(and why data is key)

I’m sure you’re all familiar with the ‘fab four’ of digital assistants, Siri, Cortana, Google and Alexa. Love them or hate them, these digital personal assistants are just the tip of the iceberg. The potential for this technology is phenomenal, with the major tech players racing to develop the next generation of personal assistants that move beyond being purely querying tools and instead act as bone-fide real life PAs.

Technology such as that being developed by Viv, an artificial intelligence platform and IBM’s Watson, is looking to take digital assistants to the next level, using artificial intelligence algorithms and cognitive computing learning capability to actively learn and apply that knowledge across devices.

It is certainly big business, with Gartner predicting that by year-end 2016, more complex purchase decisions such as back-to-school equipment made autonomously by digital assistants will reach $2 billion dollars annually. This translates to roughly 2.5% of mobile users trusting assistants with $50 a year.

The role of data

While technology and innovation in terms of A.I, cognitive computing and the IoT are lauded as the key facilitators and drivers of digital assistants, enabling, for example, automatic ordering of groceries when your fridge senses you are running low on essentials or sending gifts to family members for their birthdays unprompted.

What is often overlooked, however, is the hugely important role that data plays here. Every decision a digital assistant makes needs to be based on data. Without access to, and good integration between accurate and relevant data, none of the above is possible.

Changing relationships

As digital assistants develop and take more control of day to day purchasing decisions for individuals, questions will be raised about the relationships that brands need to develop directly with these assistants. This will dramatically alter the way brands market themselves and how they share content going forward.

Once again, data will be key here, with an opportunity for brands to ensure they rank highly. The big challenge will be how to use increasingly complex and large amounts of data for the sophisticated and autonomous decision engines that are being developed at a rapid pace.

Written by Dan Telling 

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Big Data…. is it a big deal?

How much of a big deal, is big data to the Marketing department?

So far, two factors have made big data, a bigger deal; moblification and the connected consumer, plus data value exchange. What are these terms I hear you cry?

Moblification and the Connected Customer: Customers and consumers seem to be ‘always on’ and have access to brands and businesses at all times. In a click of a button, customers can purchase, experience and complain, all within the space of a few minutes. Their behaviours can be tracked and captured across digital and physical domains through all variety of data capturing devices.  On the other hand, this also means that brands can directly address these customers at all times and places.

Data Value Exchange: Customers and consumers are much more aware of the value of their data and the multiple forms it can take. This means that when they share information, they expect for it to be used in the right way. Organisations need not be flawless in their approach, but if things do go wrong, customers expect to be acknowledged quickly! 

So what steps and actions can organisations follow to ensure the correct use of big data?

Firstly, review who has access to the data within the organisation and how readily available it is. Organisations should be aware if the data is stored outside the business with third parties. How easily can it be interrogated?

Secondly, assess who will have permission and the full use of data. Even within an organisation, the ability to use the data may be constrained by why it is being captured and under what guise.

Thirdly, consider who will digest the data. Do you have the right people to make sense of the data? There are a lot of analysts out there but not necessarily those that are commercially aware.

Lastly, organisations need to have the correct tools required to make sense of the data. Is your business thinking from top to bottom about the importance and value of data; the skills, process and culture?

Only once these questions have been answered, should you consider moving forward with the consideration of other factors, such as how to store and keep data secure and whether the data is actually relevant.

Any organisation that puts the customer experience first will be using data and big data well. I like what Ryanair is doing with its whole transformation and the myRyanair application, and this is having a positive effect on the customer’s perception of the brand. Microsoft and Amazon are other examples of businesses responding to big data well They seamlessly use given, assumed and implied data to make an experience relevant and timely; a mix of product, third party data and understanding of multiple persona’ (work, social and private). This blend of organisational, third party and customer data requires multiple functions of the business to work together seamlessly, as part of a ‘customer centric ecosystem’.

Talk to us about your data and the challenges or barriers.

Written by Dan Telling


Navigating the rapids of marketing operations

All too often that initial enthusiasm and creativity buzz of a new marketing concept is squashed due to internal blockers, disjointed processes, teams working in silos and stakeholder conflict.  As time to market drags, marketing relevance fades, as does your will to live…It doesn’t have to be this way though.

Marketing operations should be an opportunity accelerator, guiding the marketing organisation into providing real value to the company, by supporting the whole enterprise into better working practices allowing revenue opportunities to be met.


“I cannot say whether things will get better if we change; what I can say is they must change if they are to get better”.Georg C. Lichtenberg

Convincing people to change is one thing, getting them to actually change is something else.  They get the invite, they listen, there is a reassuring agreement that the overall process needs change.  But how easy is it to slip back into the comfort zone and existing ways of working?


The high-level steps to deliver a piece of marketing could be considered the same across any industry, a concept is born; worked on; signed off; then deployed.  Simple, isn’t it?

  1. Concept
  2. Creation
  3. Approvals
  4. Deployment


If you can apply such simplistic logic across industries why can’t that be applied within the same organisation across marketing teams?  How has this process become so different, complex and administrative heavy?  Unfortunately, all to easily.

Teams, or even individuals, create their own processes, which evolve over time to suit their needs.  Specific documentation is generated; different technologies are utilised; and procedure knowledge to answer why we produce something has since left the building…

Although seemingly working in silo that is rarely the case.  Interaction across teams and departments such as finance, legal, and external agencies are still happening its just that how those interactions happen is not efficient.

A single organisation working with a single agency, providing a completely different set of documentation to them depending on what internal team it came from.  Sounds crazy, but it happens.

A collaborative effort

Enforcing a new process that has had little involvement from the individuals it will directly impact is doomed for failure.

Bring the teams together, understand their current working practices: –

  • What process do they follow?
  • Who do they interact with?
  • What are their constraints?
  • What documents are created, when and for whom are they intended?


Challenge the as-is

This process is by no means easy, push to understand the rationale behind why something is done.  That spreadsheet you complete for department X, what do they actually do with it?

Agree the to-be

Design the new consistent approach: –

  • Define the marketing process – Providing each entity with what they need when they need it
  • Limit documentation – essential detail only to allow development, report on and audit
  • Remove duplication of effort – if you have already provided information don’t recreate it
  • Technology is your friend – process automation, collaboration, auditable



The introduction of new operational processes and sometimes new technology to manage operations can cause uncertainty for individuals.  A couple hours training and a ‘get on with it’ is not enough.

It is imperative to have a support structure in place to eliminate any challenges as soon as they occur.  Confidence and trust in anything new can be quickly be lost and a negative perception can transform into reality.


“Any change, even a change for the better, is always accompanied by drawbacks and discomforts”. Arnold Bennett

Hands up who has been guilty of saying “It was easier the old way”?  It can be too easy to turn against change and lose sight of the bigger picture before giving it a chance.  No-one should expect to get it right first time around.


Strike a balance of process stability and continual improvements.  Continue to evolve operations with the business, don’t let the operation restrict the business from growing.  Listen to what is or is not working, bring people together again, develop and tweak where necessary.

It may not be easy, but it is certainly achievable! Talk to us about Marketing Operations

Written by Ben Wyatt

Data governance now? Mañana!!

“See your data as a corporate asset.”

This is the long-standing war cry of the proponents of data value management.

Just like any asset you should attach cost and value to your data. You should make the necessary investments to ensure you can manage the desired yield from it.  Some analysts propose that the data asset should appear on an organisation’s balance sheet.

How many organisations are actually doing any of this?  A small minority of market leaders.

But, the majority only consider data in this way when a specific requirement rears its head.  Often this will be a regulatory or technology driven change.

This is understandable.  The cost of entry into the data value management club can be high.  There are software costs, management consulting costs and technical implementation costs. The promise of return on investment from data governance needs to be cast iron.

The temptation to wait until a project demands better data management is commonplace.  But project-thinking can mean data governance and lifecycle management processes happen in a ‘siloed’ fashion.

There is another problem too.  It is a hackneyed question but apposite in this context; “What does good look like?”  The response is typically difficult to define.  What is good for a pharmaceutical company may be quite different to what is good for a retailer.

The question also exposes a failing in traditional approaches to data value management projects.  Common wisdom would ask an organisation to consider, say, people process and technology.

Have you identified all the stakeholders, the steering committee and nominated the data stewards?

Have you defined data related rules and processes?  Have you implemented data quality related processes and assigned decision rights and accountabilities?

Have you standardised data models, database designs and leveraged service oriented architecture?

Important though these considerations are, the missing question is, “where’s the value?”

This is where two key factors come into their own.

The first is measurement.  Don’t they say if you want to improve something you must measure it first?  Build a data governance scorecard.  But, build it with someone with experience in your industry.  That way you can benchmark your organisation and see if your strategy is working over time.  It will also help focus minds on how the team’s efforts are having an effect and on what matters most.  Measure visible success not simply the work done.  Measuring in this way will also help to secure the requisite business buy-in.  You are measuring business value, not some abstruse data task.  It also helps to establish a common language when discussing data between the business and IT.

The second factor is experience – for which there is no substitute. Here, that means choosing people with experience in delivering a true value-driven approach.  Preferably in your industry. People who understand what value means to you and have the experience to deliver the results as business value.

Finally, remember data value management for your glittering new data lake too.  Assuming that the data lake will deliver value straight off the bat may be wishful thinking. Technologies like Hadoop, machine learning and graph databases will take you so far.  A data value management approach will help to measure the value and govern the data.  As a result it could prevent investments that don’t drive core business value.  In short, it will stop you creating a costly data cesspit!

Remember none of this needs to be scary.  Talk to us about our approach to data value management and how quickly we can get you to the value.

Written by Dominic Bridgman


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How do you use Technology to Optimise Marketing?

When I was asked write a post on how to use marketing optimisation technology it raised a few questions with me:

  1. What is marketing optimisation?
  2. What are people doing today?
  3. How can technology help?
  4. What are the potential barriers to leveraging that technology?


What is marketing optimisation?

We’re all familiar with “right offer, right time”; it’s the marketing phrase that was drummed into me when I first started out (twenty and a bit years ago). The difference today is the scale of marketing messages and channels that our target audiences are exposed to and interact with. We are constantly fighting for a share of their wallet, whilst also fighting to ensure our marketing budgets are being used in the best way.

We do this by:

  • Developing a multitude of different strategies and approaches for each of our campaigns.
  • Implementing contact clash and fatigue rules, to ensure we don’t over market.
  • Investing heavily in subject line testing and design to ensure our messages are opened.
  • Creating compelling and relevant offers for each of our campaigns
  • Creating multiple target cells so that we can analyse what worked and what didn’t
  • Targeting those most likely to convert with rich and relevant content to drive response rates
  • Capping volumes to keep costs down
  • Incorporating propensity scores into campaigns to cherry pick the best people to send the offers to.

All of this contributes to optimal marketing, but often it misses one major component, putting it all into practice at the same time, across all of our touch points and all of our customer engagements, to make sure we maximise response whilst minimising spend. Marketing optimisation applies this logic and capability across all campaigns at the same time.


What are people doing today?

There are two common manual approaches used to get closer to the marketing optimisation goal:

  • Campaign first come, first served – campaigns are executed and selections reduced based on who executes their campaign selections first
  • Offer Prioritisation – campaigns are all executed but outputs are pushed to a staging table. The offers in each campaign are ranked or prioritised to enable multiple offers to be pushed per campaign.

Both of these approaches do give the marketer a level of control over which customers or offers are selected, but they are prioritisation methods solving the “fill problem”, not optimisation (i.e. solving the “best offer problem”).

Prioritisation is also typically product-focused rather than customer focused.


How can technology help?

Technology such as IBM’s Contact Optimization module demonstrates how technology can be used to create and develop the processes needed to move towards a more optimised customer communication strategy.

Contact lists and campaign outputs are pushed into the optimisation technology, alongside supporting scores, rules and constraints. An optimisation algorithm then applies all the rules across the data at the same time to identify the best blend of all these targeted lists, offers, scores, business rules and constraints to determine the best average score for all the qualifying campaigns. When it’s complete, you have a refined list of contacts and offer associations per campaign, and are ready to generate final outputs.


In summary

Marketing optimisation technology can give marketing organisations the ability to create a highly targeted, highly relevant, highly efficient marketing communication strategy across campaigns. It enables us to manipulate all the levers of our customer engagements to find the right way to maximise our marketing effectiveness, without breaking the bank.

We also have to remember that it is not a magic bullet; it requires a co-ordinated and willing business drive to change. Done right, it has the potential to deliver real and measurable business and commercial benefit

The mantra should be “right offer, right time, right campaign, right people, right channel” – but that really doesn’t trip off the tongue!

Written by Andrew Addison


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