Evaluate your data asset through customer journey analytics

Corporate officials highlighted on a street explaining importance of real-time data

The secret to ongoing profitability are three little words “love your customer”. This is not just because of the purchases they make, but the behavioural data they leave behind. In the age of the data-driven business, this is where you will find insights that can be leveraged for acquiring new customers and maintaining existing ones.

You need to do whatever is necessary to keep existing customers on board. But when you have aggressive growth targets to meet, the only way to achieve meaningful uplift is by acquiring new customers. To succeed you need to be more creative in the way that you analyse your customer information and understand and utilise your data assets. Any one of your existing customers is a goldmine of information – if you know how to unlock and analyse the underlying data. Especially if you have the capability to analyse all of your customers’ behaviour.

Looking at the bigger picture, you can identify common trends and experiences that can be leveraged to attract new clients. By building cohorts of customers based on similar behaviours for instance, you can create marketing (and retention) strategies that are tailored to customer interests, preferences and behaviour profiles. Done properly, analytics can enable companies to reach that highly desired segment of one whereby each customer is understood and serviced as an individual.

Understanding your customer journey is critical to gaining insight into customer behaviour. In order to do this successfully you must understand the data footprints that illustrate customer journeys – only then will you be able to measure performance and success.

Marketers have long known that customer journeys are multi-stage affairs. But by performing advanced analytics on their data stores, the journey is shown to be made up of data footprints left by customers on their paths. Where the entire journey is digital, tools like Google Analytics make it very easy to identify and follow these footprints, tracking clicks and page navigation across your website.

But if your customer journey crosses multiple channels – online, phone, social media – it becomes more difficult to create an accurate, comprehensive oversight. Not least because each footprint will typically be recorded in a different system. You must have a way to query and aggregate each of these datasets to properly understand the various nuances of the journey.

 

What is customer journey mapping?

As we’ve already implied, customer journey mapping is the process by which customers go from brand new prospect, making a final purchase, ongoing consumption of the product/service, all the way through to the next buying cycle. In order to fully understand your customer’s journey you must also identify the data assets that document (or ‘record’) their experiences, decisions and behaviours.

Here at Idiro this is done by carrying out a deep-dive data asset discovery project to help identify what data assets are available within an organisation and how they might be used to drive value. Mining those data sets allows you to track customers across all of your channels, providing a granular view into every decision point and outcome. We then put these insights to work to understand which journeys are the most effective for achieving your commercial objectives; customer acquisition, customer value increase and customer retention.

Any business can perform a customer journey mapping exercise – even those still developing their analytics or customer management programs. All you need is access to skilled, experienced analytics experts, and their tried and tested methodologies.

Customer Journey Mapping diagram explaining the AIDA model


Moving beyond Post Its

The leap from journey map to actionable insight is not always so straight forward however. Sometimes your most valuable data asset is not the most obvious – in most cases it will be a correlation of multiple data sources.

All the data you need for behavioural analysis is available, but you may need specialist skills and tools to extract those insights and to perform data visualisation. Querying multiple data sets and collating results to piece together the fine details of the customer journey can be complicated – and potentially time-consuming.


Looking further afield

It may be that some of the data sets you identify exist outside your organisation. Examining these external sources of data can be difficult – especially when you don’t know exactly what you are looking for. Social media is a rich source of data relating to product/service experiences and referrals – but you need to know how to collect, aggregate and analyse relevant data.

The data asset audit of the journey map will also point you in the direction – you can then outsource the physical analytics tasks to experts like Idiro who have the tools and experience to analyse internal and external data sets.


Going social

Another source of data ripe for analysis is social media. With more than 2 billion active users who are sharing experiences, thoughts and glimpses of their everyday lives, Twitter is a great place to gain additional understanding of your target market because data is freely available to marketers for behavioural and intent analysis. And if you can begin matching social media profiles with contact names, you instantly gain a head-start on your sales leads.

Social media analysis also provides a way to gauge customer sentiment towards any subject of interest. This could be your brand, your products and services, or your competitors. Sentiment analysis provides another point on the customer journey map – and some insights on how to guide new prospects towards your brand.

Are people complaining about their current supplier for instance? Do they use negative language in their status updates? These are clear indicators of an unhappy customer – and an opportunity to poach them.

Once you have identified specific individuals (or similar groups of individuals) you can use your customer journey map to target messaging and draw them into your sales funnel.

Twitter, LinkedIn and Instagram offer similar opportunities – assuming you have the right social media analytics in place. Or a suitably experienced data mining partner.


A worthy investment

Never assume that the cost of predictive analytics and customer journey mapping is too high, or that you can simply “muddle” your way through. Because after all, you are entering a market that has incumbents – and you are going to have to entice most of your customers away from them.

To do this you will need to expand your data horizons to include third party information. Doing so will enrich your understanding of your marketplace and the potential customers that inhabit it. Not only will you better connect with new prospects, but the behavioural insights will provide another part of the puzzle for understanding existing clients, allowing you to further refine your customer retention strategies.

Businesses are quickly realising that advanced analytics is a crucial tool for managing the customer journey, and using their own behaviour to provide a better quality of service – and to maximise revenue earning opportunities. Making better use of the data you have is vital to love your existing customer, and to help find new ones.

To learn more about advanced analytics and using third party data to enhance the accuracy and quality of the insights you generate, please call us now on +353 1671 9036

Idiro researcher holds seminar on Social Network Analysis in telecommunications

Academic research has always been important to Idiro – that’s how we became world leaders in the application of Social Network Analysis (SNA) techniques to telecommunications business problems.  It is also why our CEO is on the board of CeADAR, the Irish Centre for Applied Data Analytics Research.

So we are happy to report that our colleague Davide Cellai, who has been working on advanced uses of SNA in solving telecommunications business problems, this week gave a seminar at the University of Aalto, Finland on his advanced SNA churn research.  Davide says:

“Last Thursday I was invited to give a seminar at the University of Aalto, near Helsinki. This week, I have been hosted by the group of Jari Saramaki and Kimmo Kaski, who were possibly the first researchers to focus a research group in social network analysis applied to telecommunications, several years ago. I presented our work on port-out churn, plus some percolation models of robustness of infrastructures I have been involved with in recent years.  Here are some examples:

Social Network Analysis in telecommunications - research by Idiro Analytics
Distribution of fraction of time two subscribers spend speaking to each others in an interval of 12 weeks

In order to profile the type of connection between pairs of subscribers, we calculate the amount of time two individuals spend at peak (i.e. during working hours) and off-peak time of the day. For each pair, we can evaluate if conversations occur mainly at peak or off-peak time and give a score accordingly. In the first figure, we show the distribution of this score over all the phone calls that occurred in a period of 12 weeks. We can see two strong peaks, representing calls occurring exclusively off-peak and at peak times, respectively. We also show an enlargement of the central zone, with a broad hill representing pairs of subscribers who speak both at peak and off-peak time. This polarization suggested us to identify three layers of acquaintances: peak, off-peak, and mixed peak/off-peak.

Social Network Analysis in telecommunications - research by Idiro Analytics
Probability that a subscriber churns as a function of churning subscribers in her network of social acquaintances
This plot shows the probability that a subscriber churns after m_out of her friends have churned in a recent time interval. Generally speaking, we can see that a subscriber with more churning friends is more likely to churn, as the red crosses tend to grow with m_out. Moreover, we can see that the probability of churning is higher for the mixed peak/off-peak layer (purple boxes), meaning that at this level of relationships, churning propagates more easily.
The seminar participants were quite interested in the way we could identify types of social acquaintance based on the time of calls. They also suggested that exposure to churn in terms of duration, instead of number of churners, would improve the sensitivity of the method. In particular, Jari Saramaki, who has also experience in the data analytics industry, envisions that machine learning methods should be fed with this kind of insightful social network information to produce best lift.

The week I have spent here has been very useful. For example, researchers have shown me a method to identify families that doesn’t use any community finding algorithm, and a way to map a temporal network into a network of events, that can be treated with a known formalism. Another post-doc is working with psychiatrists to detect the onset of a mental disorder in the pattern of social activities of a person. Finally, it has been also interesting to hear that a few students or post-docs are starting a company. Best of luck, and thank you Aalto!”

Idiro gratefully acknowledges the support of Science Foundation Ireland in this work.  Idiro works with telecommunications companies across the world, helping them with customer retention, customer acquisition etc.  To learn more about Idiro’s work on Social Network Analysis in telecommunications, or to find out how Idiro helps telcos to get better marketing results through our SNA models, contact our experts.

Some research tidbits for Christmas

Over the last couple of weeks a few interesting research items on social psychology and social network analysis have crossed our desks – so we have compiled them into this collection of research tidbits for Christmas.  Enjoy!

In-flight influence

First up, a study that shows how the decisions of people around us influence our decisions, even if we don’t know the people.  This elegant piece of analysis, written up in this working paper and covered by the Washington Post (albeit with a misleading headline) shows how our decisions about whether to purchase in-flight food and drink are influenced by those around us.   Because the study had access to reservations data, it was able to exclude groups travelling together, and control for parameters such as seat choice.

The research found that people sitting near other purchasers were 30% more likely to make in-flight purchases.  If this is the the level of influence that strangers hold over us, how much more is our behaviour influenced by those who we care about?  Answer: in Idiro’s experience, lots.

The same Washington Post article referred to an interesting piece of research demonstrating the power of peer pressure in schools.  Message to all parents: make sure your kids are in classes with people cleverer and more diligent than them.

A link analysis of languages

Multi-lingual Wikipedia editors: which languages?
Multi-lingual Wikipedia editors: which languages?

There are plenty of studies showing how which languages are spoken by the greatest number of people, which languages are economically the most powerful – but which languages serve as the pivots between other, less popular languages?  To put it another way, if you speak a minority language (like Welsh) and want to understand it written in another (e.g. Kikuyu), which other languages are necessary to make the link?  In this case, most Welsh speakers know English, as do many Kikuyu speakers – so the answer is simple: just English.

Quartz published details of an interesting MIT study looking at this in depth, using three data sources: multi-lingual Wikipedia editors, multi-lingual Twitter accounts, and book translations.  The data is displayed in an interactive website but it’s worth watching this video, as it’s a complex enough study.

One can criticise the data sources, of course (for example, the great firewall of China restricts Chinese Twitter usage) but nevertheless it’s a fascinating topic.  Here in  multi-cultural Idiro, the most common hub language is English (of course), followed, we observe, by Russian.

How many friends?

How many people do we have contact with through our mobile phones?  Idiro’s researchers took a week’s worth of connection data from a European mobile phone network, and counted the number of different phones that each person had contact with over a week.  We then plotted the distribution of the number of contacts each phone had – in other words, the total number of links per person.  As the graph shows, a number of phones were (as one would expect) used rarely or not at all that week.  A few users made over sixty unique connections in a week, and a large number of people made between 5 and 15 connections.  We compared Christmas with an average summer week. and found – no surprise – that people make more connections over Christmas week, as we renew old friendships.

Distribution of the number of mobile contacts per person
Distribution of the number of mobile contacts per person

Finally, here is a study by Hill and Dunbar demonstrating that Christmas card networks are (or were, when we used to send Christmas card to all our friends) a reasonable approximation of Dunbar’s number – 150.

Merry Christmas to all, from the Idiro team

People and data; a truly symbiotic relationship

141201.unbiased

We need data and it needs us. Every day, businesses are using data to learn more about their customers, better-target their marketing campaigns and increase their ROI. The right data can do a lot for your business, but without the right people to analyse, interpret and utilise it, your business goals can suffer.

A problem users of data face sometimes is that they are guilty of ‘cherry-picking’ the data they need to support ideas and opinions that they already have. As put in this article by Tom Fishburne: “Data doesn’t have biases. It’s people who collect and select the data who bring bias to it.”

It’s imperative to your business goals that you and your business are using your data as effectively and efficiently as possible. Here at Idiro, we not only have the skills to turn your data into actionable insights but we also have the perfect people to help you use your data to it’s highest potential.

We’ve placed SNA consultants in companies with excellent results. To learn more about our SNA Consulting services, please contact us at: experts@idiro.com

Measuring ROI Of WOM Marketing

AT&T’s Greg Pharo recently joined Ed Keller of the Keller Fay Group for a webinar on the Return-On-Investment of Word-Of-Mouth (WOM) marketing. He shared insightful information on AT&T’s research into WOM and the significant role it plays in driving new customer acquisition.

Keller stated at the beginning of the webinar that in a recent study, approximately 85% of marketers in the US couldn’t show the ROI of WOM marketing, despite plans to increase their budget spend in the category. A McKinsey article also noted WOM as being the most ‘disruptive’ marketing factor, adding that WOM is responsible for 2.1 billion daily brand impressions in the US and 440 million in the UK.

One particularly interesting statistic was that of the 90% of WOM marketing that happens offline (which is interesting in itself), over half of this was driven by one or another form of marketing or media. Of particular importance is the fact that 26% was driven by paid advertising. Targeted advertising is obviously a vital factor in driving sales through WOM and on the back of that, identifying who to target remains an increasingly important challenge. Being specialists in advanced and predictive analytics, Idiro can identify propitious customer segments so that marketers can better target their campaigns, in order to capitalise on these new figures emerging from AT&T’s research.

Although paid media remains the primary driver of sales for AT&T at 30%, WOM is a close second. Pharo elaborated on this by saying that WOM explained over 10% of sales through positive comments, but also over 10% of lost sales through negative comments.

ROI Of WOM

He concluded his thought-provoking presentation by saying that WOM metrics belonged on a CMO dashboard as a KPI and that WOM is ‘an impactful, relevant variable for influencing sales in the Wireless industry’. He believes that conversation should be a marketing objective for all marketers and Ed Keller went on to explain the best ways for those marketers to stimulate WOM:

1. Focus on ‘talk-worthy’ messages, i.e. ‘triggers’

2. Target consumers who can carry messages, i.e. ‘influencers’

3. Favour marketing/media that maximises WOM, i.e. paid advertising

He also added an interesting point at the end that maybe all media should be thought of as ‘social’.

Idiro’s expertise in predictive analytics can provide marketers with a thorough analysis of their target audience, identifying the key influencers amongst communities and even amongst families and households. Using the SNA Plus platform, marketers can really take full advantage of the power of WOM, which, if this webinar is anything to go by, will remain a key sales driver for years to come.

How to run a successful trial of Social Network Analysis for marketing

At this stage, everyone in marketing understands the power of word-of-mouth – which Tom Fishburne’s cartoon, below, elegantly illustrates. Organisations with link data – telcos, gaming companies, social networks and the like – can take a scientific approach to word-of-mouth marketing (aka influencer marketing) by deploying Social Network Analysis algorithms to target the influencers – or the influenced.   Idiro is a pioneer in this space.

Over the past few weeks we have been talking with two mobile operators who, prior to talking to Idiro, had each run projects to evaluate the benefit of Social Network Analysis (SNA) for improving targeting in marketing.  However, in both cases the trials ran into difficulties that could have been avoided. At the end of the projects both mobile operators had invested significant time and money in running a trial, but neither was in a position to make an investment decision.

We’ve been involved in mobile operator trials of Social Network Analysis for over eight years, and we’ve seen the good, the bad and the downright ugly – so we know how to run a successful trial of Social Network Analysis for marketing. Here are eight tips to help you run SNA trials that give you a clear evaluation of SNA for your business – quickly and efficiently.

1. First, be really clear on your objectives

It might sound obvious – but are you proving a technology, evaluating a vendor or trying to find the best way solve a business problem? Be really clear on this, both internally and with your SNA trial vendor(s). Also, how serious is your organisation about adopting a SNA solution if the trial succeeds?  We evaluate operators who come to us looking for SNA trials on 2 axes:

  • To what extent are the key sponsors prepared to accept the concept behind SNA for marketing?
  • The degree of organisational backing / commitment to deploying a SNA solution if it is proven (worst case: a solo run, best case: a project with board backing)

Make sure your organisation is prepared to invest in a solution before you start your evaluation of SNA.

2. Work out the evaluation, decision and implementation steps in advance

A common cause of trials not completing successfully is that the assessment of SNA that they deliver is not what the senior team needs in order to make the investment decision.  Therefore, before you finalise the trial, work out the evaluation process and success criteria. We offer our customers help with evaluation methodologies for SNA in marketing.

3. Design the trial carefully based on your objectives and your approval process

Many mobile operators make the mistake of specifying too much technical detail (while leaving the business success criteria too loose).  Others base their trial design on the offering from a particular vendor. We all know which vendor will perform best in a trial like that!

Different SNA solution vendors have different philosophies, and it is usually best not to specify the vendor’s methodology or business model tightly, at least initially, and focus on the business benefits that are required (see point 1). That way, a wide range of vendor approaches can be tested – and ideas that you did not think of can be incorporated into your project. Use the agreed evaluation method and success criteria to inform the key elements of the scope:

a) Live or historical trial, or both? b) Role and design of control groups c) Technical  / operational models to be considered (Saas, managed service, software licence, etc.)

These are important choices, and they will affect the outcome of your trial.

4. The farmer and the cowman should be friends

The most successful SNA implementations tend to have close cooperation between marketing and analytics teams. Whichever side of the organisation you work on, bring your colleagues on board early.

5. Get the trial campaign right

Because they target the influencers or the influenced in your customer base, word-of-mouth campaigns need to be designed carefully. If your evaluation involves a campaign, don’t put all your effort into the technology and test it on a bog standard campaign.  Idiro are experts in word-of-mouth campaigns.

6. Budget

Be realistic about how the relationship between spend and quality. Most vendors want to cover their costs at least, during the trial. You could doubtless persuade one or two vendors to work for free, but this might mean that you exclude the best vendors. Remember also to budget for internal costs.

7. Fix a realistic timescale

SNA trials with thorough methodologies take time to do properly. Trials with highly aggressive deadlines nearly always overrun – typically because one or more internal tasks do not receive the priority they need. Set realistic deadlines and make sure your internal project manager has the authority to get the tasks done.  Beware of shortcuts, particularly around evaluations.

8. A successful introduction of new technology requires change in the organisation, which isn’t easy

A successful post-trial implementation leading to a strong ongoing ROI depends on getting a number of factors right – operational, analytical, process change, KPIs, etc.  When post-trial implementations fail, they do so because they don’t address these difficult issues or don’t have a strong leader keeping the focus on the benefits.  Once the SNA trial is completed, the benefits are proven and the contract is signed, make sure you task the team with delivering the benefits within (say) 6 months and not just completing the implementation project.

 

Idiro would be happy to expand on any of these points.  If you are planning a trial of Social Network Analysis solutions for marketing, feel free to run your ideas by us. We might save you some heartache.

Targeting groups of influencers with music festival VIP tickets

The warmer weather in Northern Europe has put some of us Idiro folk in mind of music festivals.  That reminds us of how we helped a mobile operator not so long ago.

This mobile operator customer of Idiro’s, like many of its peers, sponsors music festivals and concerts in order to build brand preference – and to reward loyal customers.

Historically, the telco gave VIP tickets away to customers chosen for their high spend or long tenure.  However, a perennial problem with free tickets is that many more customers will say yes to free VIP tickets than will actually turn up on the day.  Typically this operator was only seeing 35% redemption of its VIP tickets, leading to big empty spaces in the VIP area, a poorer experience for VIP guests leading to the brand experience diluted for those who did turn up, and embarrassment all round the marketing team.

The customer approached Idiro with a request to use Social Network Analysis techniques to choose the best targets for the offer of VIP tickets, to achieve the following objectives:

  • Increase the % of VIP customers who show up to the concert
  • Target influencers within the base with these VIP tickets, to maximise the positive word-of-mouth from the VIP experience.

Idiro tackled the problem by using the Idiro Social Model to identify influencers with strong social ties to other influencers on the base.  In addition, a number of other approaches were also used to identify the right sort of groups of the most appropriate influencers for the task in hand.

We took the trouble to find small groups of influencers who knew each other and met the telco’s spend and tenure criteria for being a VIP.

And the results?  The mobile operator targeted these customers with VIP tickets for the next music event – and found that the percentage of VIP customers who took advantage of the offer doubled, to 70%.

Measuring the word-of-mouth benefits of a campaign like this is difficult – because the effects can be very subtle.  Nevertheless, Idiro’s customer was delighted with these results.

To learn more about how Idiro’s advanced community marketing analytics can benefit you, please contact Idiro.