The queues have grown out of control, with some non-European flyers having to wait two and a half hours to get into the country. Official time limits were broken 107 times during the first 15 days of April.

Migration officers had to be flown in (luckily on internal flights from Manchester or they might not have got through control and onto the booths in time before their shift ended) meaning they only worked 4 hours out of 8 on Border Control.  80% of The Olympic Games’ visitors are expected to arrive via Heathrow, equating to 45% more passengers and at this rate people might not make it to the events!  At least there may be some spare seats for those not lucky enough to receive any tickets in the ballot though!

So what’s gone wrong?

Downing Street blames the delays on the wet weather but BAA says that the Home Office is to blame for enforcing tougher security checks (which take a longer time) and staff cuts.

Surely OR analysis would have shown that these staff cuts would prevent the process from running smoothly; and would have been used to derive optimal staffing patterns with those remaining to meet the demand rather than shipping extras in last minute.

Even a simplified queuing theory model (such as an M/M/c) taking arrivals as independent (when of course they are not as passengers come off planes in groups) could have shown that reducing the staff numbers, whilst increasing the service time on passport checks would result in these long queues.  Otherwise, some more sophisticated event based stochastic simulation could have given an indication of the outcome, taking into account the probability of different weather conditions and likelihood of delay, with separate models for EU/non-EU travellers.

Figure 1 shows the average arrival figures (data taken from US Customs and Border Protection for between May 2010-2012) over the course of a day for passengers in Chicago O’Hare airport. This airport was chosen as US data (but not UK) is readily available and it is one of the biggest in America, so comparable to Heathrow.  However, it is a smaller version compared with Heathrow’s average of 4,000* arrivals per hour and so the graph would need to be scaled up (but a similar trend expected).

The green dashed line gives an indication of how delays to planes could affect arrivals in a simplistic form, based on the relationship with the number of booths open. In reality, delays would cause much more distortion to the data, forming higher peaks and troughs to put stress on the Border Control.

If we assume that staff are working at capacity during the busiest time (between 3 and 4pm), then it takes 1.5 minutes for each person to get through Border Control on average.  This means to get 4,000 customers through in an hour, 100 border staff are needed.

Linear programming can fit shift patterns around these predicted arrivals trends as closely as possible, ensuring that the available staff are used in the best possible way.

So with more sophisticated OR techniques, (maybe they even were carried out but not taken notice of) this situation could have been much less severe.

To any of you who are flying off tonight to make the most of the long weekend, I wish you all the best and hope the queues do not spoil your break!

*http://www.heathrowairport.com/about-us/facts-and-figures

Photo taken by Herry Lawford

I tried to get in the mood on the flight over by watching the film Moneyball – a good example of analytics being used to gain a competitive advantage. Instead of ‘gut feel’ recommendations from talent scouts, the film tells the story of Oakland Rangers, a baseball team, who despite their disadvantaged financial position and ability to attract the top talent, employed deep analysis of player statistics to recruit an optimal and winning team. Its good to see Brad Pitt putting his faith in the numbers and analytics. Moving away from a ballpark, evidenced based recruitment decisions is an example of Workforce Analytics that can be used by all organisations. For example, at Capgemini, we recently helped a client develop optimal recruitment plans by analysing the future evolution of their workforce. Analytics itself is no guarantee of success (as Damien Comolli recently found out at Liverpool…) It’s important to understand the full organisational change impacts – we do this by combining business analytics consultants with HR experts from our Employee Transformation team.

Having landed, I got to the hotel and couldn’t find highlights from the afternoons football. Instead there were at least 5 different ice hockey games being televised as part of the Stanley Cup – the ‘Champions League’ of ice hockey. The match I watched went down to the wire:

Blackhawks Even Series, Defeat Phoenix 4-3 In OT: Chicago down by 1. On the road. Seconds to go. 6 attackers on the ice. Where have we seen this before? For the second consecutive game, the Blackhawks net a late game-tying goal with just 5.5 remaining on the clock coming off the stick of Brent Seabrook and tipped in by Patrick Sharp sending the game into overtime.

Essentially Chicago got a goal with 5.5 seconds remaining by switching (pulling) their goalie for an extra attacker. But they only just managed it with 5 seconds to spare and given they dominated possession and created many more chances playing with an extra attacker, I was thinking surely they should have done this earlier to give them a longer time to equalise. A quick look on the internet and you realise that hockey and baseball are studied by operational researchers and analytic professionals just as cricket and football are analysed by Duckworth, Lewis and your armchair fan with Cricinfo and Opta Statistics in the UK. One paper has studied the ‘pulling the goalie’ strategy to see if the coach’s gut feel is correct:

               “If you’re down by one goal, you’re looking at the minute mark”

The paper shows how the “one minute, when trailing by one” rule does not stack up when analysing the statistics. Using poisson processes for goal scoring and analysing the impact of both scoring and conceded goals when playing with an extra attacker and without a goalie, the model shows the optimal to pull the goalie is with around 2 ½ minutes remaining:

I am reliably informed, and I guess its not too difficult to see, that if you told a coach that your analysis was supported by models and statistics, the baseball coach is far more likely to listen than the hockey coach. Probably again like cricket and football.  An Ice hockey player will never listen to your statistics, just as Ronaldo doesn’t take note that his best strategy is to shoot the penalty kick straight down the middle

Although I understand little else about hockey, I can now watch the matches with a little more interest as the game draws to a close and as I pound away on the hotel treadmill trying to pick out a silly little puck on the screen in front of me.

So, how will you be choosing your horse?

I have a full proof plan; I look at the age, nationality and weight allowance of the horse to cut down the list to a more reasonable number. There are 40 horses and it just isn’t in the spirit of the race to put a pound on them all. Age is slightly irrelevant this year as the minimum for entry has changed; Irish is best, occasionally the French get a look in but I like to go with my home country of Ireland; and lastly, the horse should not have a handicap of too high or too low a weight, but higher weights are becoming more successful. These rules normally whittle the numbers down to about 15 horses. From the remaining horses, I read up on their past performances; how they have performed over similar distances and then ultimately, pick the ones with the names I like. As a true analytics professional, I know where to draw the line when it comes to chance, in this situation – there are 25 lines to be aware of, otherwise known as fences.

Of course, you can just follow the pundits’ tips to make your choices. The BBC has collated their selections (BBC pundits). Of course, they are all different, with the most common horses predicted to win: Chicago Grey, On His Own, Always Right and Killyglen with two first place predictions each. However, Cappa Blue was placed 5 times out of 12 and Killyglen, West End Rocker and Ballabriggs all being named 4 times each in various places by this selection of tipsters. With Killyglen’s Irish roots, it may well be getting my backing following this review; of course this is all for fun and we are not responsible for any betting pattern you decide upon.

Place your bets…

So once the horses are chosen – by foolproof algorithms, pretty jockey colours or otherwise; it’s time to place your bets. It was once traditional to head down to the bookies to place that annual bet, but now it seems that as a nation we are moving much more towards the digital age when it comes to betting.
Many of the major bookmakers have launched online platforms to expand their channels; however they do not see this channel as a strict alternative to their retail stores and telephone betting systems, but as a situational choice for their punters. Since the launch of their online platforms, the net revenue generated by their telephony systems has decreased steadily; however the growth in net revenue through online has more than made up for this.

Bookmakers are transforming with the changing digital world. Mobile betting is now taking off, and not just in a singular way. Betting apps, text features and mobile games are all methods that are being used to try and take advantage of the fascination with mobiles.

With an estimated £2.5billion gross win (revenue after payout) in the gambling industry, it is no wonder that bookkeepers are keeping up with the latest trends and technologies to attract their customers. An estimated 10% of UK adults bet on a regular basis. There are only 5 main companies present on the high street but there are many more online and through telephony services. It is also known that punters are not a loyal breed outside of the retail area, therefore it is important for these companies to provide platforms that will continue to attract their regular customers, as well as the increasing passing trade of multiple accounts betting punter.

The betting companies know their customers; many of their online customers are also using the mobile applications. Many of the punters using these methods are educated males under 35, so the applications are targeted at this market. Customer accounts provide a wealth of information to be able to carry out customer analytics; using this information the companies are able to tailor their offerings to a target market based on past performances. This is all in the power of customer analytics.

Flex Benefits: One of the lovely benefits of the job, with options including travel insurance, Dental cover, and even shopping vouchers…a real perk that we want to take advantage of right?. Yet why is it, that even though we have well over 8,500 hours (that’s a year) to consider what flex benefits (we would like to choose… and a whole 500 hours (that’s 3 weeks) to register our preferred options… I’m still sat at my computer with 1 hour (that’s 1 hour) to go before the flex benefits deadline and am completely befuddled as to what options to choose!!

My main point of confusion this time around – do I get a company car?
My faithful 12 year old VW Polo is giving up the ghost – she has to have an ignition coil replaced every other month, one of her lights looks like she’s got a cataract and the RAC are on speed dial. After the humiliation of the rescue van coming out to me on Halloween when I was dressed as a “Zombie Schoolgirl”, enough is enough!

But as the company cars sparkle at me from our Flex Benefits options – I can’t help but wonder….is it really the best option for me?
So I used my pre-deadline hour wisely and did my research in true Operational Research and Business Analytics style – I took a mid range car (the Audi A3) and did the maths:
What are the factors to consider?:
• A Company Car means a brand new sweet-smelling vehicle, with pretty much everything included. But it comes at a pretty hefty monthly cost , its going to depreciate really quickly and you don’t own the car at the end of the agreement
• Buying second hand means it will be cheaper to start with (or I can get a better car for the same price), it wont depreciate as quickly but I’ll need to pay for tax, repairs and insurance
I think my Business Analytics colleagues would call this an Options Appraisal. So, with the help of autotrader.com I’ve had a little go at creating a mini model to cost this up.

The graph attached shows what we all know – that new cars depreciate really quickly – but my analysis has helped me to actually quantify this depreciation for the type of car that I’m considering.
So what have I concluded?
I’m not buying a new car – it will smell nice but it loses value WAY too quickly to be worthwhile. So with that off the table, what of the other options…

Both company car and buying second hand are viable options, what it really comes down to is do I have the cash to pay up front for a 2nd hand car, or would it be better for me to just begin with monthly payments on the company car scheme…to be decided 
What I do know is: I’m now an Audi A3 expert and I don’t want one. One day I aspire to own a big family car with room for a couple of car seats and a pram… but right now I’m on autotrader.com looking at cars with funky cup holders and a roof that goes down.

When you check out at The Cooperative and they scan your loyalty card, sometimes, seemingly at random the till prints out a coupon for your next visit, “Spend £20 and get £2 off” or perhaps £30 and £3. Until recently I collected my 10% from time to time and didn’t think much of it. Now, however, every time I shop a new coupon prints, and I have a collection forming at home. So what’s going on here?

While I referred to it as “seemingly at random”, as a member of the Capgemini Business Analytics team, I should know that a supermarket will have sophisticated customer analytics working behind the scenes, especially when working with invaluable loyalty card data. So why am I suddenly being wooed with discounts? What does my behaviour look like in the data?

My partner and I do all of our shopping at The Cooperative, it’s literally across the street from where we live. My partner and I also travel extensively for work and pleasure. Since Christmas we have both worked at least Monday to Thursday out of town, and spent several weekends on the continent. Our spending profile appears to have simply dried up. Are we shopping at another supermarket?! Presumably The Cooperative’s customer analytics have identified us as a loyalty risk, and are now throwing discount coupons at us to try and win us back. Fascinating.

So, am I won back? Fact is, I’m not a loyalty risk at all. While I am a price sensitive consumer, the only threat would be someone opening an even more convenient supermarket, and that would be pretty difficult given the current across-the-street circumstances. It’s still nice to feel valued, though, and certainly nice to see an organisation behaving intelligently with customer analytics.

And just for fun, consider the possibility that you could two-time your supermarket. Get yourself set-up with a couple of different loyalty cards, and cycle between them, creating a constant impression of loyalty-risk. You risk this kind of gaming behaviour any time you create unusual incentives, particularly when an “unfair” situation arises where perhaps truly loyal customers are not offered discounts and are thus worse off. I doubt anybody but the most hardcore follower of moneysavingexpert will actually try this, however.

Each sporting activity within the Games appears to have its own individual twist on qualification procedures. Some have single set criteria, some have multiple criteria. An example of this is in Athletics. Around 2,000 athletes are expected to perform at the Games. To make it fair on the different National Olympic Committees (NOCs) – a maximum quota per NOC is applied according to the standard achieved by the individual athletes’ performance. To achieve ‘Standard A’, athletes must achieve a certain time. To achieve ‘Standard B’, athletes must achieve a lower standard time (for the appropriate athletics event, replace time with distance or height). Within any NOC, only a maximum of three ‘Standard A’ athletes can be entered (plus one reserve ‘Standard A’) and only one ‘Standard B’ athlete (plus one reserve ‘Standard B’). These performances need to have occurred at any one of a range of competitions that are deemed to follow the qualification event rules e.g. no wind-assisted performances, hand held stopwatches are not appropriate for distances under 400m.

Due to the restrictive nature of these quotas, it is therefore not the end of the story for athletes in NOCs that contain more athletes that qualify than the quota allowed. Here, these athletes must continue to perform at a high level to prove their place in a team is deserved more than that of their fellow competitors.

Conversely, consideration has been given to NOCs who may not have the strongest athletic ability. If there are no qualifiers from an NOC, the best representative from their NOC who did not meet the ‘Standard B’ requirements can be nominated. It is then up to the IAAF (International Association of Athletics Federations) to judge the candidate’s technical level and reach a decision on entry to the competition.

These are just an overall gist of athletic qualification requirements; some events have further specified rules. For instance, in relays there is no ‘Standard A’ or ‘Standard B’ as each NOCs can only enter one team. The top 16 teams are decided based on their combined best two races during the qualification period where there are at least three international teams competing. Marathon and Race Walk do not need to achieve the set time, but have World Championships were the top 10 or 20 (depending on which championship) places are considered to be ‘Standard A’ qualifiers.

Moving on to other sporting events within the Games which have different qualification criteria. Take qualifying in canoeing which can seem a harsh reality. Qualification can only be achieved by top performances at the world championships and the remaining slots filled from the relevant continental championships. This may mean that a team from a different continent which you have beaten in a world competition could go through with a worse performance than you based on continent championships. This is not the harshest part, in canoeing the participants are not claiming a place for themselves in the Olympics, but a place for their country meaning their country has the right to select a different competitor to perform at the Olympics.

These are just a couple of brief examples of the complexities of the qualification system. If you want a non complex event, go for tennis. There are no particular qualifying events; every country has the right to enter their highest ranked player. In addition, hosting the Games has the benefit of guaranteed quotas meaning that some entrants of the host nation do not need to qualify in order to guarantee their place. Great Britain is of course attempting to achieve the ‘normal’ qualification despite their free pass in some sports.

If the qualification process for the Olympics was represented by an Excel model it would be a series of entry points ruled by the random events with distribution functions, the ranges are known but not the exact number. Each entry point represents a prospective qualifier and the random event is their outcome in their qualifying event. This then passes through a series of ‘IF’, ‘AND’ and ‘OR’ functions (the qualification rules) to capture the many routes and possibilities that could occur. Running this model repeatedly would give a series of different outcomes, which in sport keeps the ending exciting.

Very often in business, the ending should not be kept exciting. Companies can have complexities built in as standard practice without realising how complex they can be. Change and improvement can therefore be difficult to attain and understand internally. This is where the logical approach of our business analysts comes into its own. Not only by breaking down the complexities, removing them if possible or, where necessary, building these complexities into a working model but also understanding the inputs to the model.

Understanding what will have the greatest impact on the outcomes will allow for greater control and improve the ability and confidence to agree on important business decisions.

Many of these questions will be answered with varying degrees of accuracy in bars and pubs across the nation. Usain Bolt will probably win three gold medals for Jamaica if he does not false start; the American basketball team will probably retain the men’s basketball gold medal; Michael Phelps might not win seven gold medals in London, but only a brave person will bet against him winning a handful; you’d probably expect Jessica Ennis to win a heptathlon medal for Team GB. Drawing on analytical methods can however provide an alternative basis for an estimate for overall medal performance .

Using an econometrics model can help to predict Olympic performance by investigating factors or variables that could have an influence on a countries’ medal count. The influences of the identified variables are estimated by adopting the Classical Linear Regression Model based on the 2008 Beijing Olympics medal count, shown below.

Y = β0 +1.95G +0.003P +39.2H + 0.91C +ε
Below are an explanation of the variables that are used in the Classical Linear Regression Model.

Economic Resources (G) – A cursory look at the Beijing 2008 Olympics medal table will quickly reveal that the top four countries by gold medals won are permanent members of the UN Security Council; France the last permanent member came in the top 10. It seems safe to infer that economic resources are a determinant of performance. Richer countries are able to afford to spend more on better training regimes. As a proxy for economic resources we will adopt the GDP per capita.

Population(P) – Additionally, a large population increases the potential group of athletes and enhances the chances of a nation winning more medals. An example is China with its population of well over a billion people improves the odds of producing an Olympic champion. Conversely India has a large population but poor performance in the Olympics. This variable will be the total population of each country.

Host Nation effect(H) – One can also not ignore the host country effect: host countries are allowed to participate in all events, and the majority of the crowd at the events will be rooting for the home athletes. There is also the possibility of extensive practice at the designated venues by the same home athletes. China demonstrated the power of home advantage by topping the medals table in their backyard four years ago. The host nation will be represented by dummy variables, 1 for host nation and 0 for all other countries.

Past performance(C) – Finally, the past is a good indication of the future and past performances should reflect expectations of the future model. The medal count from the Athens Olympics will be included as an explanatory variable.

Prediction(Y) – The number of medals predicted to be won.

The Classical Linear Regression Model results shows the contribution that each variable has in predicting a countries total medal count. It estimates that just being the host nation, contributes to 39 of the total medals that would be won. By applying all of the variables, the model makes the following predictions for 2012.

• Great Britain, 68 medals
• Australia, 46 medals
• France, 31 medals

This is obviously good news for the GB team. We also ran the model for the French and Australians (We didn’t have time to predict them all). Now it’s just down to the athletes to stay fit and prove the prediction right! Come on synchronised dividers and gymaths. (Sorry).

Retailers, usually the first to suffer in a downturn, have to look for new ways to invigorate their market and gain sustainable advantage over their competitors. One option is the creation and use of some of their vast amounts of customer data that are already available, and becoming easier to collate as technology provides different ways of capturing people’s behaviour.

The answer is retail analytics . Retail analytics advocates the use of data to get a better understanding of customers and, crucially, predict their behaviour, help retail companies to differentiate themselves from competitors, and fuel growth in challenging markets. The wealth of data available to retailers presents the opportunity for competitive advantage, but only for those who exploit it most effectively. There is no point generating so much data and allowing it to lie fallow.

Retail analytics has at its core the process of taking data from multiple sources of the retailer’s business and translating this intelligence into performance improvement. Historically, these data sources have included loyalty programmes, point of sales terminals, market surveys and market research. And some of the rewards have been enormous. Take Tesco for example: they used the date from their Clubcard loyalty programme to build unique and insightful customer profiles, to the point where it now has 12 million unique profiles for its 15 million customers (Competing on Analytics: The New Science of Winning).

These data sources will continue to retain their relevance as customer retention initiatives could be the difference between keeping customers happy or losing customers. The current challenging environment and increasing competition, however, mean there is need to progress into further areas and drawing on other inferences. A retailer can achieve growth by being able to predict the behaviours of their own loyal customers; even more growth is achievable by being able to predict the behaviours of the customers they do not currently have.

Breaking new ground in gathering useful information will inevitably involve internet-driven data gathering. Web browsing histories and online purchasing habits have become an invaluable source of data. The likes of Amazon use these data sources to support their recommendation features to their customers, while other retailers undertake targeted advertising using consumer purchasing patterns and information gathered on the internet.

Extracting datasets from social networking sites and fusing those with data already available will help retail analytics break new ground. Facebook and Twitter have together over 1.1 billion users (Facebook 845m, Twitter 300m), with Facebook reporting that their average user spends 6 hours 51 minutes per month on the site. This is a data minefield for the evolution of retail analytics and taking advantage of all that data to operate and manage the business better requires analytics.

Online retailers have shown dexterity in targeted promotions and awareness based on most recent purchase. For example, a customer buying a golf club is targeted with additional golf equipment, perhaps at a discount to reflect his loyalty, before exiting the check out.
It is in this area that in-store analytics may be behind the curve. A customer will rarely receive any promotions based on her shopping basket and will not be targeted before leaving the store. However, with better real-time availability of shopping information, it is possible to offer products and promotions on the spot. For example, certain product purchases are supplementary and could well be made together, though the purchaser may not realise it yet, and letting her walk out of the store may mean the business goes elsewhere.

Examples of Winning with Retail Analytics (source: Babson Executive Education- Realizing the Potential of Retail Analytics – Plenty of Food for Those with the Appetite)

Tesco has profited greatly from the introduction of its loyalty card program ClubCard. 80% of Tesco’s sales can be tracked through ClubCard providing rebates of 1% of customer purchases. In addition to the rebate, customized coupons based on shopper behaviour are provided to customers.
Online retailer Amazon.com has an aggressive program to identify and prevent credit card fraud, which in its first six months led to 50% reductions in fraud.

Amazon uses a scoring approach to identify the most likely fraud situations in customer purchases. Some of the circumstances conducive to fraud include purchases of easily-resold goods on the gray market (such as electronics), the use of different billing and shipping addresses, and use of the fastest shipping option. Such variables are used to identify and prioritize cases for investigation.

Waitrose has developed a new system for store-level sales and demand forecasting. It takes into account holidays (including Pancake Day), promotions, and seasonality for predicting demand and feeding replenishment processes. Forecasts are produced for each category and SKU per day per store. Benefits from the new system include more efficient inventory levels, particularly for refrigerated products; improved accuracy in replenishment of dry goods; a 40% reduction in order changes, and less time spent by managers in forecasting, freeing them up for customer interaction.

Online retailer Amazon.com used marketing mix models and test market advertising to determine that broadcast television advertising was not cost-effective. The ads did increase sales, but not as much as other marketing approaches, including offering free shipping. Amazon stopped TV ads altogether and dismantled its five-person TV advertising department.

In response the Council has suggested a number of possible solutions:

• Double yellow lines on one side of the road
• Waiting restrictions to prevent road users parking on the road at certain periods of the day
• 24 hour controlled parking zone with permit parking for residents
• 2 hour controlled parking zone with permit parking for residents

Each of these solutions has one or more benefits such as improving the traffic flow, eliminating parking on pavements, preventing all day parking (by commuters). However, before making a decision the impacts of the solutions on the different types of road user should be considered.

• Local residents who do not commute by car and leave their car parked on the roads
• Local residents who commute by car
• Parents dropping their children off at the local school
• Suppliers / customers of local businesses
• Non-residents parking in the area to use the railway station
• Non-residents commuting into the area to work in town

This blog does not attempt to present the argument for any one solution; rather it provides some initial analysis to demonstrate the challenges that councils face when making this type of decision. To avoid turning a simple blog into a full research paper only three options are considered here; do nothing, introduce parking on one side of the road, and a combination of parking on one side of the road and mid-day waiting restrictions. The options selected would seem to be minor changes that may solve the problems but what are the impacts?

The simulation above tracks individual road users of different types as they attempt to find available parking space within in the local area. By capturing where all the individuals end up parking, compared to where they would like to, aggregate results can be used to evaluate how successful different types of road user are at finding a place to park, and whether they parked on the road they would have liked to. The above simulation was run over a 4 week period to test this.

Simulation of the current parking situation (without any restrictions) shows people are always able to find somewhere each time they attempt to park, even if it is not quite where they want it to be. Residents do well at being able to park on their preferred road when compared to other road users. Benchmarking the results above against the equivalent figures for our possible solutions provides us with some insight into what the impacts of the possible solutions will be.

The proposed solution to eliminate one of the problems, parking on pavements, is to limit parking to one side of the road in the areas where this issue occurs. Imposing this restriction on Spencer Gate and Sandridge Road solves the problem but roughly halves the parking capacity on these roads.

In systems such as this where parking availability is finely balanced, even a relatively small reduction of 33 parking places from the original 450 leads to some major changes. Now the number of residents who are able to park on the road they live is down from nearly 80% to below 60%. The reduction in parking places also means that some road users, including local residents, will be unable to find any parking in the area at all.

Like many areas near city centres and close to stations, extra pressure is placed on my area by large numbers of commuters parking on the roads during the day while they take the train into work. Arguably the council should endeavour to ensure that parking is available to local residents so the introduction of additional parking restrictions may be required to restrict the number of non-residents parking in the area.

Implementing a two hour waiting restriction on Boundary Road increases the number of commuters and local business customers/suppliers that cannot find parking to nearly 10%, but it has little impact on making more parking available to residents. With or without this restriction in place local residents are likely to make approximately 50 failed attempts to park in the area every 4 weeks

The situation improves for residents who commute to work; their chance of being able to park on their own road increases from around 50% up to 70%. However, the situation becomes worse for residents who leave their car parked on the road most of the time and only make occasional journeys; their chances of being able to park on their own road are now below 50% and they have a 5% chance of being unable to find any parking.

The analysis of these few options does not present a clear answer, but what is clear is that small changes, even to a single road can have major impacts on the availability of parking. Eliminating one problem can easily create another, and if councils want to make parking available to both residents and local business then they will have to consider their options very carefully.

It may not be a popular solution but, if other charge- free solutions prove equally ineffective, residents may have to accept the prospect of paying for permits in order to ensure there will always be space available for them to park.

It’s the Economy Stupid

I won’t bore with you the current economic climate – you know it’s not good. Eurozone uncertainty is yet another headline making us all nervous consumers – even those who are not significantly worse off as a result of the recession are determined to make their disposable income go further. I have personally found myself entering more competitions recently and actually opening emails in my inbox offering 2-for-1 deals, voucher codes and discounted tickets. As we become more cautious about how we part with our money, retailers are responding with heavy discounts – or at least the appearance of them. The latest Panorama exposé investigated the price wars between the Big 4 supermarkets in the UK and revealed some interesting facts about the ‘deals’ we are being offered to entice us to spend our precious pounds. In fact it was revealed that 42% of consumers don’t trust supermarket offers and 47% of us have felt misled by offers.

Figure 1: Clothing, Footwear and Accessories sector, Week-on-Week 2010 & 2011, IMRG Capgemini E-retail Sales Index

The British Retail Consortium’s figures for November reveal that sales performance was the lowest since May; sales values were 1.6% lower on a like-for-like basis from November last year and on a total basis, sales were up only 0.7%, against a 2.8% increase in November 2010. Retailers might not be completely disappointed though. Because Christmas falls on a Sunday this year, we have a full working week and Christmas Eve to do last-minute shopping and the two days after Christmas Day are bank holidays, leaving retailers with the hope that the second half of December will make up for the first.

Multichannel Maestros

To move away from the doom and gloom for a minute, let’s turn to technology. The way it is transforming how we interact with each other socially and professionally is also changing the way we interact with retailers. Not only do we want to shop online, we want to shop with our smartphones and tablet devices and on top of that, we expect to be able to find the same products at the same price as we’d find on the high street. In fact Experian Footfall reported that as much as 12% of online sales will be made using a smartphone this Christmas. We can also now compare prices for the same product easily online, meaning we’re doing quite a bit of research before we hit the high street, which inevitably puts pressure on retailers to drive prices down and optimise their marketing budget to get you into their store. A metric which illustrates this trend is the conversion rate, which online retailers use to understand what proportion of visitors to their site actually makes a purchase. From the IMRG Capgemini E-retail Sales Index data below, you can see that the average annual conversion rate is falling as consumers use retail websites for research purposes more and more, but the trend around Christmas time is remaining the same as retail generally increases regardless.

Figure 2: Monthly Sample Average Conversion Rates, 2009 – 2011, IMRG Capgemini E-retail Sales Index

Online sales at Christmas typically take place earlier than high street sales due to the nervousness about Christmas post. In 2010, December snowfall stung many online consumers when their orders arrived too late for Christmas, which appears to have led e-retailers to offer Christmas shopping deals a little earlier than normal this year. Early indications are that November will have been a disappointing month for e-retail. UK e-retailers refer to the busiest online shopping day of the year as ‘Cyber Monday’, which comes from the US marketing term designed to encourage online spend the Monday after Thanksgiving. Cyber Monday may have been a week later in the UK than in the US this year due to the fact that most people got paid during the last week in November. Amazon.co.uk certainly seem to think so – they have got over 100,000 products on offer this week as part of a Cyber Monday promotion, perhaps an indication of their fears about it not being as strong a week as usual. As far as the high street is concerned, footfall in November was also disappointing. The Experian UK National Retail FootFall Index reported a year on year Index for November which is trailing behind 2010 by 3%. Sectors that have been particularly badly hit have been Health and Beauty (suffering their first year-on-year fall for two years) and Clothing. Food and drink sales did increase though, perhaps a factor or the retail wars at supermarkets, or simply the fact that they are more essential purchases.

So the economy and technology are certainly changing the way we shop, of that there is no doubt. Quite how Christmas 2011 pans out for the retail industry remains to be seen though – no doubt the papers will be keeping a close eye over the next few weeks on whether it will be a case of ‘better late than never’ for retailers or whether the shrewd consumers of 2011 will keep their belts tightened until 2012.