Can Share of Social Chatter Predict Share of Market? – Update! (Topic Originally Discussed Nov 2013)

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Back in November last year we mused on the possibility that share of social chatter might correlate with current, and predict subsequent, share of market.

Well we have since learned of pioneering research by the Kingston Business School, in particular under the steerage of Professor Robert East, which suggests that it probably does.

A published by Uncles, East, and Lomax, 2010, (see footnote for details) demonstrated how levels of Word-of-Mouth discussion has a 0.8 correlation with market share.

Moreover, shares of positive WOM comments correlate at a level of 0.9, whilst negative correlates at a lower rate of 0.7. This, they explain, is because positive WOM comes mostly from current owners / users whilst negative WOM relates mostly to previous owners.

If you think about it this does make sense.

If a brand has a problem (like the famous Perrier water contamination, or the negative stories associated with Dasani) then volumes of negative chatter will rapidly out-perform market share performance, but market share may well stay low long after the volumes of negative conversations have declined.

Moreover, they quoted in 2010 that First Direct and Waitrose were getting a more positive share of WOM than their market share, whilst Tesco was getting noticeably less.

Looking back now we can see how this has translated into the subsequent fortunes of these 3 companies over the subsequent few years.

Monitoring and managing WOM is therefore a powerful tool for long term brand success.
BUT – you do not get positive WOM recommendation unless you’ve done something to deserve it.
Moreover, Professor East, in work he says is yet to be published, quotes strong evidence he has that ‘satisfaction’ is (not surprisingly) the most important antecedent of WOM chatter about 40% of the time. That is a pretty big single contributory factor.

Our research, as we have indicated before, shows that “Willingness to Recommend” is influenced by the five key drivers of brand preference.

And you can track your performance on those drivers.

And you can pull real marketing levers to manage them in a positive way.

To find out how, stay tuned to Schezzer over the coming months.

Your brand could be seriously richer as a result.

Reference: Uncles M, East R, and Lomax W (2010) Market Share is Correlated with Word of Mouth Volume; Australasian Marketing Journal 18 (p145-150).


Can Share of Social Chatter Predict Share of Market?

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Each month Marketing magazine publish their ‘Social Tracker’ revealing how differing companies and industry sectors are squaring-up on social media.

Using analyses by yomego and HumanDigital they provide interesting perspectives of who is being talked about, when and why, and consider what it may be revealing about customer/consumer/shopper/buyer opinions.

In the September 2013 issue, for example, they ran a feature on Tesco which showed the share of supermarket shopping-related chatter accounted for by the top supermarkets.

Interestingly, these shares were very much in line with the actual physical turnover market shares of the supermarkets. Not identical, but certainly enough to show a strong correlation!

So is this surprising and/or useful to know?

Well it probably isn’t that surprising if you consider that the number of people who will be moved to comment is likely to be proportional to the number of customers that a company already has. So whilst there may be short term fluctuations, take a big enough sample and long enough time frame and you’re bound to find such a correlation.

The more interesting part for marketers is whether the differences between say, share of endorsing comments and/or share of detracting comments, is going to be predictive of underlying share growth or decline. Inevitably one has to believe that it will be, and that marketers must take heed of this.

Much predictive discussion around social media has concentrated purely on the short term – “if I know you are travelling via Gatwick today can I offer you a coupon to use my restaurant chain at the airport” – but this is a poor use of the medium – anyone can obtain short term share increases by cutting prices irrespective of the medium they use.

And marketers who believe that the ‘primary goal’ of social media is to give people ‘brand experience’ are simply deluding themselves if they see that as an end in itself. The only way you can ‘experience’ a Mars bar is to eat one (or have it melt in your pocket but that is not a recommended mode of consumption).

Of more relevance is whether fundamental steams of endorsement or detraction are out of kilter with a brand’s current market share.

This has a direct analogy with an older measure of strategic marketing strength – relative accumulated weight (of advertising and/or strategic merchandising and promotional spend).

This older model of brand behaviour showed (and still does) that long term brand share trends are predicted by a brand’s relative share of accumulated spend – i.e. the sum of strategic marketing investment made (typically) over the past five year period relative to other brands in the market.

Outperform the market on this measure and your market share will rise. Underperform and your share will probably decline. Moreover, this measure can be improved upon in predictive capability by taking account of the perceptions generated through such spend on five key criteria which we call the five key dimensions of purchase motivation. These five key criteria are:

Relevancy (right solution)
Identification (right image or brand identity)
Accessibility (perceived – and actual – obtainability)
Value (right cost-benefit trade-off)
Confidence (expected reliability that the brand promise will be delivered)

The same is true of social media comments.

These can be assessed on these five dimensions and scored according to their level of endorsement or detraction. It does not matter that, on average, there will tend to be more negative comments than positive ones (people with a problem will always be more vociferous than those who haven’t) – what is important is the trend and the RELATIVE levels across brands.

It works because in many respects Social Media marketing is an example of ‘strategic merchandising and promotion’ – in much the same way as would be achieved by, for instance, Red Bull sponsoring an Air Race. Accumulate the weight and reach of the messages given and tally them relative to the competition and you have a metric for predicting future share trends.

We have been exploring this for some time now and can see that it certainly works for our clients – so far. This is a new medium and there is more to learn but the nature of human relationships with brands is more fundamental and has a longer time horizon than the various means by which it is achieved.

So next time you look at Marketing’s Social Tracker, pause for thought about what it might be saying for the probable long term health of your brand.


Analytics and the Finance Team

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A typical Monday morning in many companies involves a panic stricken Finance team rushing around at some ungodly hour desperately trying to provide explanations as to why sales are down 1% on the budget and showing 3% growth on the previous year.  Spreadsheets of increasing complexity are being pummelled into submission, powerpoints are being generated and words are being crafted in ways that do not offend delicate egos nor upset the political apple cart.  A final check to ensure all of the action points from last week have been closed down – and then it is done!  Only the world has moved on – the critical questions from last week have been superseded by other questions.  Somebody had spotted a rounding error. Nobody could remember how much buffer was built into the forecast.  What impact will the unseasonally bad weather forecast have on sales next week?  And so the chaos starts again with only a few days to turn it round in time.  And in some companies this maelstrom of chaos happens daily.

When sales and profits are growing there is little need for complex analysis – the urgency to understand the drivers of performance is more muted where the return on investment is 7% compared to the target 10%.  One of the consequences of this is that the analytical role became the preserve of finance.

Growth and profit have until recently been driven by an unrelenting focus on operational efficiency.  For this the classic Finance approach is perfect – systematic variance analysis that deconstructs performance variances and delivers clear accountability.

Most of the companies I have worked for have at various points had teams of specialist analysts, typically having maths, science and economist backgrounds.  But the number of people employed in these roles has significantly reduced in recent years.  After all, the numbers are easier to access.  IT are claiming one version of the truth and Finance can do anything nowadays in their Access databases. So why have two teams doing what is seen as the same thing, ie the numbers?  An easy call to drive operational efficiency and reduce unnecessary overheads is to reduce the analyst pool.

We have lived through an era where progress has been made through driving operational efficiency, compliance and standardisation and customer demand has been relatively strong. Is this sufficient for companies to thrive in the future?

Finance rarely look at the world from a customer perspective – it tends to be transaction based and ignores customer circumstances and attitudes.  However it is now relatively easy to identify at an individual level motivations and preferences and hence empower communication and engagement at an individual level.

The one size fits all approach to marketing is no longer relevant.  We are not simply looking at another level of sophistication with CRM – we are now at a point where a product can be tailored specifically for an individual, where a promotion is tailored based on individual preference.

 

So What are the Consequences?

  • There are a number of companies who have used analyst capability alongside classic Finance with continued success.  These companies will continue to exploit this competitive advantage.
  • For companies that have reduced their analytical talent or simply have never had this capability – with the tools that are available today it is relatively easy to play catch up.  The risk is in getting the right data from your system (which may require significant IT investment) and can find the right resource.
  • For many smaller companies this type of customer centric analysis was simply not affordable – it is now.  A good example is with regard to market research – in the past this required dedicated resource (bought in or in house) to ask customers questions.  The ability to generate online surveys at nominal cost, combined with the ability to handle large volumes of data without expensive tools such as SAS means the playing field has at least partially levelled  with the blue chip beasts.
  • The world of retail is already seeing niche operators springing up specialising in customer and/or product niches.  This is clearly happening in the analysis world as well – and this again comes back to the tool capability and cost – you no longer need excessive server and application running costs to produce the analysis and insight that you did 10 years ago.
  • Hence companies no longer need to get support from the big blue chip consultancies in these areas and can have confidence in the ability of smaller specialist operators.  It is these operators that are also working with some of the best analysts who are attracted by the flexibility of the working relationship and avoiding the red tape and bureaucracy that goes with working for a “normal” company.
  • Of these new analytical companies the ones that thrive will be the ones that can communicate clearly and effectively to ensure clients are making decisions on the best information available.  Those that fall into the old analytical trap of poor communication and analysis paralysis will simply not survive.

 


Big Data – a company’s most valuable under-utilised asset

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By 2004, Wal-Mart’s data warehouse had reached 500 terabytes of data. (A terabyte is one million million bytes). Since then, it is estimated that the volume of data being stored is increasing by 50% a year – doubling every two years, and data flows on the internet in the United States alone are approaching an annual total of 1,000 exabytes. (Two exabytes equals the total volume of written information generated worldwide annually; five exabytes equals all the words ever spoken by human beings.)

This is what is now being called the age of ‘Big Data’.

It’s not difficult to understand the reason why this is happening. We collect more and more data, particularly about our customers: all their transactions; their ordering patterns and frequency; who they are, where they are and how they behave; and what they say about us on the social networks. And we store it, because the cost of storing or transmitting a kilobyte of data is now too cheap to measure.

What is less understood is that this data is probably one of the company’s most important assets and one that is woefully under-utilised. It is a strategic resource that can be used for making better decisions.

A recent study estimated that if all the data being stored by companies globally applied ‘deep analytical’ expertise to their data, then total savings would be more than $149 billion in operational efficiency improvements alone. No wonder the World Economic Forum, at their last meeting in Davos, declared data to be a new class of economic asset, like currency or gold.

In a research study into 179 large companies published last year, it was found that those companies applying ‘data-driven decision making’ achieved productivity gains 5 to 6% higher than any other factors could explain.

So how do you mine the potential gold from this most valuable asset? How difficult will it be? Well, three simple steps will do it:

1. Identify the data that just sits in storage just because it’s there.

2. Find the analytic support that will help translate the data into useable insights.

3. Put them together and get the insights. But don’t ignore the insights – act on them.

If you take these three steps, you’re on the road to data-driven discovery and decision making.

As a meerkat once said: “Simples!”


The Price of Austerity

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Someone once said:  “Promotions are like heroin – best not to start.”  Of course, this is not true of all promotions, but it is increasingly true of price cutting promotions.  Here, it is the major retailers are becoming increasingly addicted to price cuts and most of the cost of this is being funded by their suppliers.

I have worked with one famous FMCG company who are spending £10 million a year on brand building activities such as advertising but £100 million on trade price support.  Curiously, they spent a lot of time and effort analysing the effectiveness of the £10 million spend – for example, they knew the sales response they could anticipate by changing their advertising investment – but devoted little effort to analysing the £100 million trade price support spend and how they could improve the efficiency and ROI from this.

This is not uncommon across many companies.  I suppose that more often than not this expense is regarded as a “cost of doing business” with the big retail groups and therefore unavoidable.

If it’s an unavoidable expense, then why bother with the effort of analysing it?

Even though the spend on trade price promotional support is often impossible to avoid, it still brings potential dangers in its wake, not just the danger of undermining the brand’s positioning carefully built up over the years, but also the serious danger of plunging the business into losses if the sales uplifts fail to cover the price support costs.

And given the highly competitive nature of today’s marketplace and the focus on the next promotion, the amount of time and effort devoted to analysing past activity becomes difficult to sustain.  But if you do put in the effort, the immediate returns can be quite spectacular.

The charts below help illustrate this.  This is the end result of an analysis of the effectiveness of price promotions for the same brand in two major retailers across the same time period.  The price reductions are similar, but the resultant sales uplifts vary wildly and the proportion of loss making promotions is much higher in one retailer than the other.

Comparing ROI   performance on similar price promotion programmes across two major retail   chains – on the left, mainly positive ROI performance, but on the right it is   mainly negative

This kind of analysis and comparison creates the stimulus to go further and understand why there are these differences and the determination to align the performances across retail groups and create better short term commercial returns.

Most companies that have done this and reshaped their price support plans have generated an immediate improvement in ROI of at least 15%.

There’s nothing clever about losing money on price promotions – nobody gains in the long run.  Data driven analytics helps pinpoint where you are losing money and helps you determine how to reshape you trade price support programmes to stop the losses.

The quickest way to make money is to stop losing it.  Now, why wouldn’t you want to do that?


Paying for Time or Results?

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On April 20, Coca-Cola announced that it was moving to a “value-based” remuneration system for the advertising agencies that work on its 400 brands – a reward based on sales and market share achievement. Procter & Gamble have been running performance related fees for 12 major brands for some time.

This type of remuneration system is also spreading to other service agencies – accountants and lawyers, for example.

Could it work for Sales Promotion Agencies? Wouldn’t a remuneration system based upon achievement of ROI on sales promotions rather than one based on hours worked be better? This would have the benefit of getting clients and agencies working even more closely together – aligning agency compensation with client profitability.