Ian Thurman, Vice President, Location Planning

As retail outlets have become just one element of an omni-channel world is it time for a rethink in the world of location planning? Is it all just too difficult to comprehend or does the vast array of customer data now give us the key to unlock the latest challenge in store planning?

Essentially location planners have sought to understand consumers’ spatial responses to retail locations on offer now and in the future. Over time we’ve coped with a move from a ‘one nation’ view of shoppers to an understanding of the complexities of geodemographics and the transport modes available to shoppers.

Customer guesswork?

 Store performance models for retailers now routinely include bespoke catchment models and a precise understanding of the spending potential for each consumer group. We even pat ourselves on the back for thinking about the impact of online on store performance and cheer when we have knowledge of the likely online uplift in a virgin area when a new store opens.

However as we look to meet the omni-challenge we need to re-assess how we analyse consumer behaviour and their spatial interactions with retail outlets. We need to appraise existing methods to give ourselves a realistic chance of understanding the omni-channel world. The challenge has intensified as online has become the top performing ‘store’ in most retailers’ portfolios.

Understand the ‘whole’ customer, know the trading potential

 The opportunity for a new approach lies with our understanding of the individual customer. The customer centric focus of many retailers offers the capability to solve the omni conundrum. If we are to understand the spatial behaviour of consumers we need to drill deeply into the customer data held by retailers. We have to examine each customer and their relationship with the retailer across all digital and physical touchpoints.

At best it means we know from a single customer account which store a customer uses and where the shopper accesses online for browse and purchase and by what means both physical and mobile. And above all else we need to see the colour of their money. This all presents a massive challenge for many retailers as they seek to link up customers across all channels and connect multiple accounts for the same person and household.  

A precise knowledge of individual customers, their spending and touchpoints will provide a clear picture to understand the retailer’s business from the ‘whole’ customer perspective. This approach delivers an opportunity to create individual customer models, bespoke and actionable customer segments (at CACI it’s my.acorn) together with an assessment of spend, opportunity and interaction across all touchpoints. The benefit to the business in respect of existing customers is the opportunity to target those customers whose actual spend by touchpoint is below segment expectations.

Evaluating the opportunities, valuing the prospects

 Of course, increasing spend is only one part of the picture. The other challenge for any national retailer is to identify the potential from non-customers by channel. The development of a bespoke my.acorn segmentation using customer data can be used to match to the wider population. As a result prospects can be valued by channel leading to improved return on marketing spend and new store opening and rationalisation scenarios can be accurately tested.                

Facing the omni challenge   

Omni-channel is now leading to reviews of every business process in retailing and location planning faces the same pressures. The old ways are in need of substantial re-engineering and analysts will increasingly be examining the activities of individual customers before they embark on the development of spatial models.    

On the subject of ‘omni’, a quick Google search reveals the bizarre fact that the Omni International Mall in Miami closed down in 1999 due to decreasing sales 23 years after it opened. Perhaps it’s an omen foretelling of the need to get location planning from an omni-channel perspective sorted out before it’s too late. Making the right property decision is now all about consumer, consumer, consumer. Fail to understand the omni-channel consumer and you’ll fail on location, location, location.

Ian Thurman, Vice President, Location Planning

As established players from the USA and Western Europe look to open retail outlets in the growth markets it’s worth considering the implications of those early openings in new countries.

With many retailers recognising the benefits of opening with local partners or through franchisees there is a danger that location decisions will be left to the local operator. If the franchisor retailer receives a slice of net sales without contributing to property costs there can seem little incentive to play a major role in location decisions. As a UK retailer said recently, “if we get our 15% why should we care about the location decisions of our partners”.

Similarly for those opening fully-owned stores a new country can tempt the business down the route of ‘let’s open stores and see what happens’. Particularly if property costs are lower than in the western markets.  

Can you afford to open the wrong location?

Such an approach ignores the main point about location decisions – they can’t be easily discarded. Whether it’s a purpose built store or a shopping centre lease, it’s a long term decision. Location choices result in significant long-term investment and carry important cannibalisation implications for the future network.

Even Tesco who have led the way in selecting supermarket locations have faced problems in choosing store locations. In the USA they have closed over 10% of their Fresh & Easy stores principally because of location selection issues. 

Don’t be a cannibal  

Using a UK example, opening a major store in London Kensington creates a block on a more profitable opening at Westfield London less than 2 miles away. In this example the stores operating under the same fascia would divide trade to such an extent that neither would be profitable.

Similarly, in Moscow with 6 major shopping centres to choose from it’s unlikely that a department store would want to open in more than 4 centres to avoid excessive catchment overlap. The initial selections could preclude a successful opening in the most appropriate shopping centres. Making the wrong decisions could create a sub-optimal network for the Moscow market for years to come.  

A failure to create a ‘road map’ for store openings will result in an initial chase for space, the wrong location decisions, and loss of the best locations in the future. Poor early decisions can also result in early withdrawal from a market that should have been profitable. Why is it important to make the right decision – because even if the brand owner is only getting 15% of sales – 15% of $10m is much more interesting than 15% of $5m.

Can you afford to get it wrong?

Ian Thurman, Vice President, Location Planning

TIME FOR HARD HATS?

As even the mighty M&S struggle to achieve sales growth in their UK stores, executives in British retailers are heading for the most difficult decisions of their corporate lives. Those retailers still achieving success are being underpinned by growth in their online business. Increased sales per sq ft in pure ‘bricks’ operations are becoming a retail rarity.

Across the patch, large store portfolios seem like they belong to history. And chances are that bricks’ performance will get worse under the double whammy of falling consumer confidence and a move towards more online activity.

For many UK retailers it’s now time for serious pruning of the store portfolio. But where do they start? And what are the implications of injudicious cuts? The most difficult decisions often carry the greatest risks and rationalising the store portfolio will be fraught with difficulty. The right choices will minimise the retailer’s overall turnover losses and the wrong decisions will have a much greater impact than expected.

CLICK & CLOSURE OR CLICK AND CHAOS?

Some will think the decision is easy. Open up the spreadsheet, click on data sort and close down the stores with poor sales against allocated costs. And expect that customers will transfer to their nearest store. However with multi-channel retailing and the complexities of UK consumer geography this is no time for back of the envelope calculations. Successful rationalisation will only happen by undertaking a detailed review of the UK portfolio and a thorough assessment of the place of bricks in the multi-channel equation.

FOCUSING ON THE CUSTOMER, NOT THE STORE

Store rationalisation runs the risk of focussing on the store rather than the customer. It’s important to remember that the store is merely a transaction point for customers. It can’t be assumed that the store (and by association the retailer) owns its existing customers. Particularly when retailers often know more about their online customers acquired in recent times compared to store customers who have been nameless shoppers over a number of years.

Store closures will not force changes in customers shopping patterns and so it’s vital to model the resultant loss in trade and identify stores and channels to capture displaced shoppers.

FLYING THE HIGH STREET FLAG

The internal relationship between stores and online is now much more than the operational issues over click and collect facilities. Any closure process needs to take into account the effect of closure on online trade from local shoppers.

Our consultancy work has shown a ‘brand anchor’ online benefit for many retailers in terms of existing stores and new openings. We’ve seen uplifts of up to 15% in online business in a new store’s catchment area (see Paul Langston’s earlier blog).

On the flip side, whilst store closures might not have a substantial and immediate effect on online business, scenarios need to include online decline as brand anchors are removed from the gaze of local consumers. The warning is clear – close a store and you may lose online business in the area, never mind struggling to hold on to existing bricks-based customers.

TRANSFERRING THE BUSINESS

Time was when the height of CRM in relation to store closures was the hand-written notice in the window instructing customers to go to the nearest store. With closures across the UK it will be vital to minimise the effect of closure on shopper’s spending. Reliance on staff on threat of redundancy to market the replacement stores and online channels will be asking turkeys to vote for Christmas.

It’s never too late to start collecting customer data from store customers and without that data retailers run the risk of losing their customers’ spending following closure of their local store.

Whilst closing stores may be soundly based in financial reality it’s also worth remembering the need to counter local PR issues. The fickle British consumer had abandoned branch based banking in droves but never failed to support the fight against their local closures. Marketing offers to displaced shoppers needs to be in place as soon as closures are announced. And at a national level don’t forget that loyalty can be focussed on the individual store as much as the retailer brand.

MAKING IT HAPPEN

The rationalisation process needs to be both strategically sound, tested and carefully actioned. In addition to the operational and logistical issues, our process follows the following 10 steps for a successful rationalisation process.

10 steps for rationalisation

1. Review customer shopping patterns around stores (and collect customer data if required)

2. Identify stores with highest customer overlap

3. Predict level of brick-based trading after initial rationalisation scenario

4. Identify stores still likely to fail to achieve satisfactory performance

5. Agree closure & re-size list (subject to lease issues)

6. Assess impact on online sales in closure areas

7. Identify customers (on & off-line) affected by the closures

8. Create multi-channel customer retention strategies for displaced shoppers

9. Close & re-size stores & act on retention

10. Review store performance, online sales and customer shopping patterns against predictions

Rationalisation for UK store portfolios is on the way. The only question is how you do it. Will you be the next retail disaster movie or a reputation enhanced by a carefully executed plan to focus stores on their local demographic?

Paul Langston, Consulting Partner, Location Strategy

Despite some major headlines in recent months that have cited the Internet as a factor behind store consolidations, retail stores are not going out of fashion!  Yes, some retailers are struggling, but others are still very much on the growth trail, even when they have high and growing levels of online sales.

Yes, online sales can have a major impact on store viability.

We’ve seen in our work with a number of clients that this relationship can be positive, and that a store presence can improve online sales.  This is especially the case for retailers with a strong brand, clear online proposition and supporting links between their on and off-line offers.

We have seen some big-name casualties where the Internet was a major factor.  But, in my view, the impact of the Internet can be a convenient excuse, when in reality there are other pressures bearing down on retailers’ expensive store networks.

For many retailers the threat from the Internet and new technologies is no worse than the many other retail dynamics that are impacting on every store – ranging from shifting store pitch to changes in the national economy.

So that retailers are not caught out by these shifts, I believe that ongoing Network Management is essential to ensuring the long-term viability of all store networks.  This involves; regularly monitoring the unique factors at play on each store, alongside traditional metrics like profit and like-for-likes.  Only by understanding the Store Potential, Network Interaction, Store Position and level of Online Interaction acting on each individual store can retailers make truly informed decisions about their estate.

There is no shame in closing stores in a managed way in response to these shifting dynamics, and an ongoing pruning of the estate is an essential part of maintaining a healthy and vibrant store network.

Ananya Sadera, Head of Client Services, CACI

Instigating projects

Customer analysis and insight can help companies in a variety of ways from simply understanding who are buying their products and services, to more advanced techniques such as calculating lifetime value and share of wallet. Furthermore, this information can then be used to find and acquire prospects who look like valuable loyal customers. Simply put, insight is business critical.

However, in many companies Analysis Managers have walked into roles “where it’s purely a support function doing management information and report after report.” While the intention is to provide proactive insight, because of changing businessdemands, the reality is that most analytic teams end up being more of a reactive/supportive function.

Richard Tomlinson, Head of Analysis, CACI

However, when analysis teams action repetitive requests, they are unable to spend time and provide the little nuggets that can be truly ground breaking, the little details that tell them not only which customers are leaving but why, when and what are all the different variables that lead to this event. Nuggets that might provide direction to the business by identifying new markets or potential threats.

To avoid having a team fall into this cycle, one recommendation is to structure the analysis team into two functions – one responsible for identifying and reporting on the key customer questions regularly asked by marketing teams, and a separate team responsible for proactively driving additional insight. Separating the two provides the bandwith and time for the insight team to instigate true insight.

Managing data analytics projects

Data analysis projects come with their challenges and the following tips provide some ways of getting around these:

1)     Data availability

We have seen a number projects that have not come to fruition because data availability is only discussed after project sign off. Only then does it become apparent that the customer or market data required is not readily available from core systems and so timescales will have to be totally revised. Always check to see if the data sets that are to be the building blocks for your project are available. For example, in-bound call centre data is usually key for retention modelling and in most organisations is not linked to the marketing database.

2)     Stakeholder management – know your audience

If the key stakeholders are from a business background, make sure the results are based on business relevent questions and not too technical. They might not be interested in code! Similarly if it’s a technical audience that has ultimate sign off make sure anything they want to know, such as the variables used and model strengths, are addressed. For example, for a marketing audience, presenting a profile of the top decile of a propensity model will be received better than a gini coefficient!

3)     Regular feedback meetings

A common occurrence on these projects are instances where there is a huge gap in what companies percieve as their customer base to what it actually is. Regular meetings during a segmentation project and the sharing of findings as you go along ensures there are no surprises at the end of a project and concerns are addressed as you proceed. For example, a sophisticated clustering algorithm may identify a niche group of high value, older and low affluent customers. Can the business work with a such a group? Is it of sufficient size to tailor propostions to?

4)     Rescoring and rebuilding 

A short time after a segmentation or modeling exercise, comes the question of refreshes and updates. Be clear at the outset on terminology:

Rescoring or Refreshing: Using the current algorithm to update a customers score or segment.

Updating or Rebuilding: Revisiting the original project and refining or totally re-creating the algorithms.

Agree at the outset on the timescales and on how often these will takeplace. It may be relevant to re-score in real-time in some high customer activity organisations such as the telecoms sector, although quarterly re-scoring is the norm.

The typical lifetime (before update or rebuild) of a model or segmenation is 2-3 years, but it is prudent to check on a quarterly basis that the solution is still fit for purpose and the profiles and performance of the algorithms are still in line with what was seen when they were first built.

Analysis and insight are like a puzzle, putting the different pieces together to complete a picture. The key is having a structure in place that allows this to be a focus and managing information so when the final picture is revealed there are no surprises!

SOCIAL MEDIA IS EVERYWHERE

Social Media has yet to have a definition truly nailed down. Wikipedia, an apt source for this information, states that “Social Media are media for social interaction, using highly accessible and scalable communication techniques”… which is interesting. It means that ANY medium through which a two-way conversation can take place counts as a Social Medium which, in turn, means that the pub counts.

This is an interesting (and I believe accurate) perspective as the logical conclusion we can arrive at is that not much has changed between pre and post Social Media: We are still, effectively, talking about “word of mouth” except, this time, it is on steroids. Whereas in the past a conversation in a pub might influence a handful of people, it is now possible to influence tens of millions of people. Herein lies the power of Web 2.0 Social Media: The voice of the customer is now broadcast, linked and amplified to a far greater extent than ever before.

Its not just 140 character “tweets” that the comments are made through. Facebook updates (and updates on other social networks), YouTube videos and Forum comments also add to the noisy conversations that are happening 24/7.

When I ask clients whether or not they use Social Media, they often respond along the lines of “Do I look like I use Social Media?” but upon further probing, most do indeed use Social Media. Most people think that Social Media are the same thing as Social Networks… not so. Most of the clients accept that they use Social Media when I point out that Amazon, Ebay and Google all have social elements and if they have used any of the reviews (or other User Generated Content), then they are, indeed, users.

The reason I have spent so long in explaining what Social Media really are - is so that it is understood just how much UGC there is “out there”. No single corporate entity can afford to store all of the Social data that is produced by users -there is just too much – so how do companies make it manageable and leverage the reams of data?

SOCIAL MEDIA MONITORING

Social Media Monitoring tools have been around for some years now, but are only just starting to really get to grips with the volume and diversity of content. Essentially, they all work by trawling the internet for specific triggers and then pulling back the information, rather than storing it all in an enormous database and then running queries. The main difference to the user of one of these tools is that it is easier to start a search and allow it to run into the future than it is to try to go “back in time” through searching stored information, unless the information is stored elsewhere. Sites such as Twitter, however, don’t keep the “tweets” for long, though, so running a search to measure the effectiveness of a new product launch, for example, has to be done right first time… there are no “re-dos”.

Its not just keywords, though, that the budding Social Media monitoring employee should think about. Software is now advanced enough as to understand sentiment to a useful degree of accuracy, and even sarcasm… which is soooooo easy for us humans to pick up, but much harder for machines. The mechanism for flagging remarks as sarcastic is long, though, and complicated and a subject for another blog post sometime, but the point is that these tools are well established and can whittle the billions of comments made every day down to those that are relevant to you or your brand at which point, I would suggest, a human being deals with them.

If you decide not to use any of these tools, you could watch Twitter, for example, 24 hours a day, but what about the other 500+ major social media channels? Its going to be expensive to hire the manpower necessary.

Fig. 1: Social Media Monitoring Tool classes

So, what vendors are the best ones for you? That’s a tricky question. When I last counted how many Social Media monitoring tools there were “out there”, I got to 168, each of which has its own strengths and weaknesses to evaluate, which is not the purpose of this blog post. The purpose is to address how best to use Social Media monitoring tools to analyse Web 2.0 data (or so it says in the title).

USING SOCIAL MEDIA MONITORING TOOLS TO ANALYSE WEB 2.0 DATA

There are two ways that these tools can be used to enhance a company’s insight on customers, both current and prospective.

1) Monitor the public activity of known individuals and this, in most cases, requires some kind of opt-in from them and is thus hard to achieve (but immensely valuable).

2) Address customers and prospective customers as one entity and see how people are reacting, in real-time, to various stimuli en mass.

This approach could, for example, give a good indicator as to the impact a new advertising campaign is having on discussion volume and sentiment and provide feedback as to how to improve it … or whether to remove it.  If taken to the extreme, this data can be mapped and segmented into very precise, small segments which, with the aid of a representative sample of the population, be mapped back to your own customer data… giving new levels of customer understanding to the data analysts. Let me give an example: Customer data indicates that I am a male, I am 27 years old and I earn £200k a year (I wish!). It also shows that I live in west London and drive a motorbike. Finally, it states that I spend £100 a month with your company. With the online profiling just mentioned, it is possible to see what online behaviours others that share these characteristics have, such as hobbies, holiday destinations, favourite cartoon, least liked food takeaway type, political leanings and anything else you might want to know.

It really is that powerful if handled right.

Why would you want that kind of modelled information on a customer?

Knowledge is power - power to stop bombarding customers with irrelevant marketing (and thus power to market more effectively), power to design products and services around what your customers are likely to want, not what you think they will want, power to engage with customers about things they care about, not what you think they care about but overall, it’s the power to listen and let your customers know that you are listening.

Since customers that feel a company cares about their opinion sell around 50% more effectively than companies that don’t, I think the argument for Social Media Monitoring pretty much sells itself, don’t you?

The main reasons behind the growth of online CRM include more accurate targeting, more predictable ROI and the availability of free web analytics reporting tools such as Google Analytics (GA). With GA, you can track and measure many types of online campaigns including social media, and conduct A/B and multivariate testing. GA is also capable of tracking and reporting offline campaigns such as phone and TV campaigns, creating an affordable platform for integrated marketing.

But GA metrics are available only at an aggregated level, which means you cannot readily integrate it with offline CRM systems. Based on our recent investigation, this blog post outlines the steps to break down the aggregated metrics into individual visitor and session level data and how to export and integrate it with the offline CRM systems.

There are three steps involved:-

1. Extracting the visitor and session level data from the GA tracking process
2. Exporting the web metrics at visitor and session level
3. Pass data to the offline CRM databases

1. Extracting the visitor and session level data – you can modify the existing GA javascript tracking code to include a new custom variable that collects the visitor and session level data from first party visitor cookies that is written/read by GA on the customer’s PC. The data is stored in GA servers and you will need to access it through GA data export API (explained in the next step).

2. Exporting the web metrics – the visitor or session level data collected in the above step can be accessed via the GA data export API after authentication through the main GA account. You can export the data as a text file which will enable it to be integrated into most offline CRM systems.

3. Pass data to the offline CRM databases – Once you have the data as a text file, the online data can be matched to the offline data with identifiable information such as surname and postcode. This information can be either collected from the order transactions or when the visitor submits some type of form or engages in some mechanism that includes identifiable information.

How do you fix the results to achieve critical mass?

Critical Mass can be achieved in several different ways.

These include the following:

Leverage existing networks and relationships

The closer the relationships that individuals have with others, the more frequent the communication becomes because there is more in common, more shared time together, more shared experiences and so on. Thus starting a social network amongst an already-strong network, where conversations are already happening regularly and relationships are strong, is an important factor in achieving the frequency of interaction (F) that is required to get to critical mass.

Furthermore, the closer the relationships, the greater the probability that an individual has a high level of influence (I) over the audience. The influence of the user is not directly related to the content itself, however, but it is important to the success of the network.  

This is because those with influence have a stronger “pull” on peers. Influence is based on trust that is built up over numerous interactions. In essence, influence is based on the reputation of the individual making the comment.

Restrict the topics of conversation

By decreasing the scope of conversation that can be held through the social network, you ensure that a higher proportion of the comments are relevant to the individuals that are using it. Plumbers may not, for example, be interested in hairdressing tips, but would be more likely to be interested in plumbing knowhow.

Demonstrate the value that users achieve

By showing off the value that is provided by the social network as publicly as possible, you ensure that the message is able to be passed on to others, thus increasing the saturation of a community and progressing towards critical mass. Showing photos, leads, connections, friends, “likes” or any other statistic is a reasonably public way that encourages others to join to reap the same rewards. This also triggers competitiveness amongst some users, further increasing the rate of adoption.

In order to be a big fish, shrink the size of the pool

Critical mass is essential to the success of a social network, but it  is not dependant on the size of the target community. Facebook reached saturation within a single university before it spread to a second, a third and, eventually, opened up to the rest of the university world. Only after Facebook had reached saturation within the student communities did it open its doors to the world.

The principle behind this is that at each enlargement of the community, the saturation never dropped below the critical mass.

Think of a bucket that has been filled with water. This equates to the Harvard University Facebook saturation. The bucket is full and so cannot hold any more water – every student that will use Facebook now does. If, however, the bucket is doubled in size, by adding another university, for example, there is now room to pour in more water once again.

If the increase in the size of the bucket (community population) does not result in the new bucket being less that 15% full, this bucket can now fill up until it, too, is full. This process can, and did, continue, bucket by bucket, until Facebook became the phenomenon it is today.

If, however, the population saturation (amount of water in the bucket) had fallen below 15%, the Facebook we know may have collapsed in its infancy. [1]

Filling the first, small bucket

The equation for good content is complex because N (Community population size) is actually comprised of Users and Employees – those that are independent of the company and those that are subject to direction – such that:

N = U + E

N = Community population size, U = Users (non employees), E = Employees

NB: U and E are mutually exclusive

The way to ensure that the seed network that is started reaches critical mass, is to increase E as much as possible by mandating participation from employees. By controlling the majority of the population on the network, you can fix the frequency, the value and the relevance, not to mention the decreased distance of relationship between co-workers compared to strangers.

This approach takes careful planning, strategy, training, policies etc so as to ensure that the communications are transparent, legally compliant, relevant and of value, but can help an entity achieve critical mass.

Although difficult to overcome, it is absolutely possible to grow a social network from nothing to critical mass organically. Success is, however, absolutely reliant on getting it right first time, as users are fickle and are significantly less likely to log in a second time if burned, for whatever reason, the first time.

The trick is to ensure that each of the communities reaches a high enough saturation that the inclusion of new communities doesn’t dilute the content to below the critical mass required.

Influencing the equation

P = Perceived User Value I = Influence  F = Frequency   R = Relevance  U = Uniqueness  V = Value  D = Distance of relationship  N = Community population size.

The approach should be carefully planned in advance and should involve deep thinking in the following areas: Overall Strategy, People and Training, Process and Policy, Technology and Tools and Data and Reporting.

How does a fledgling social network develop the content needed to achieve critical mass?

Consumers will make repeat visits to a website because they find that it consistently delivers value to the user in one of the four areas, outlined below in Fig.1. However, the reason that people visit social networks on a repeat basis is slightly more complex.

Fig. 1 The four pillars of online value (V) delivery channels

The true value (V) of the site is the sum of the four forms of capital, outlined in the table above, but it is only one element of the considerations that are made when users evaluate a social network:

Perceived User Value (P) for a user is a factor of the content itself as well as the frequency (F) at which the content refreshes. The strength (distance) of relationship - (R) that the contributor has with the user is also important, along with the actual value of the site.

It can be defined as:

 P = I(F x R x U x V)/(D x N)

P = Perceived User Value, I = Influence,  F = Frequency,   R = Relevance, U = Uniqueness,  V = Value,  D = Distance of relationship,  N = Community population size

It should be noted that Perceived User Value (P) itself is comprised of four components:

• Perceived competitive positioning – “I think this is the best”
• Perceived associations – “Others have perceptions of this, and I do/do not want to be associated with these things”
• Customer need – “This does what I need it to do”
• Perceived proposition delivery – “The exchange works for me”

In order to ensure that a site achieves the 15% opt in from a community needed to achieve critical mass, as discussed in the previous blog, the content must be good, but, initially, at least, frequency (F) will be low.

This is because there are few contributors, and distance of relationship is likely to be high as the probability of any user chosen at random throughout a population being closely affiliated to another randomly chosen individual is slim.

There are, however, techniques that can be used to sway the equation in your favour. Critical Mass is achieved by increasing the numerator in the above equation and decreasing the denominator. Check out our next blog to find out ways to do this effectively.

The full white paper on Achieving Critial Mass in Social Networks can be downloaded here.

What is Critical Mass within a community and how does it seed the success of a new social network?

The creation of online social networks is now a process that is accompanied by technologies that are so advanced that what once required the expertise of the best programmers can now be bought off the shelf and personalised in as little as an afternoon.

The difficulty with the creation of a social network still remains, however, with the achievement of Critical Mass – that moment where there are enough users of the network to produce enough content on a daily basis to keep users logging in and contributing themselves.

Critical Mass depends on a number of different factors, but it is also important to understand the limitations within which social networks must work.

Historically, studies have shown that the maximum number of people that can be sustained in a community where each relationship is maintained is around 150. Robert Dunbar performed a number of calculations based on the relative neocortical sizes of species and added credence to this number for humans (now known as Dunbar’s number). Examples can be found in Neolithic village size upper limits, Roman army divisions, the splitting point for Hutterite settlements and, more recently, the proposed optimal company size for the military.

The same is true for online social networks. The maximum number of relationships that can be actively managed by an individual is about 150 and the average is around 130. More interestingly, however, the number of friendships that are seen to be managed through social networks is between 10-15%[1] of the number of relationships an individual has – the average Facebook User with 130 friends will leave only 6 comments per day on others’ photos, posts or statuses, indicating that not all of the 130 relationships are friendships.

Comparisons across Network Theory, Graph Theory and real-life examples of technology adoption show that after around 15% of a community has been penetrated, the rate of acceleration of adoption dramatically increases until it plateaus at a saturation point.

How Community Penetration Determines Interconnectedness of Social Networks

Adoption Rates of New Technologies in the US

Thus the immediate goal of any Social Network is to hit that 15% saturation of the community they are targeting. This raises the problem of how best to address Critical Mass when the target community is, for example, the population of the UK. To achieve 15% of 60,000,000 (the approximate population of the UK), 9,000,000 people need to sign up (and use regularly) before Critical Mass is achieved – a prohibitively high threshold.

Herein lies the crux of the difficulties surrounding achieving Critical Mass for a social network: in order for people to visit a site more than once, there must be good content. In order to get good content on to a social site, you need people to be visiting the site frequently.

Therefore, once you have enough people (Critical Mass), they will produce enough content to ensure return custom and thus further good content and increased usage in a self-perpetuating cycle, if not influenced by exterior forces, such as competition.

The full white paper on Achieving Critial Mass in Social Networks can be downloaded here.