Australian Abroad, Keen Capoeirista, Museum Mogul, Budding Blogger, Thirsty Traveller – currently Itapuã, Salvador, Brazil
We collect data about who visits and engages with our collections in a number of different ways. We participate in a nation-wide benchmarking survey programme for visitor attractions; we conduct our own bespoke surveys for exhibitions, special events and marketing campaigns; sometimes we use volunteers to conduct 1 or 2 question surveys on the door as visitors enter; and we gather data through systems such as our Wi-Fi sign up and ticket sales.
We are constantly examining and analysing this data to give us an up to date insight into what is going on: who’s visiting, who isn’t, what motivates them to visit, and how well do we deliver against visitor expectations.
At the start of every year I always carve out some time to pull together all the data we have collected across the year so that we can have a comprehensive year on year analysis.
I keep all our data in raw form in a *very* large excel spreadsheet – I would like to use some of the more powerful data crunching software available, but budgets don’t usually extend that far. I’m a bit of an excel geek, and I find that if you have the raw data there isn’t much you can’t extract using excel.
I cut up the data in lots of different ways to see what I can learn: is there significant differences in the satisfaction ratings reported by different types of visitors? Is there a correlation between motivation for visiting and visitor spend?
The one type of data I always spend a lot of time on is postcode and country of origin. This piece of data is collected in 99% of our data collection cases, as there are so many things it can be used for.
Country of origin data for tourists helps us decide which languages to prioritise when developing multilingual content – this has been important for a recent multilingual audioguide project (which I will share more about once we launch). The data on country of origin we collect via most of our surveys has to be taken with a grain of salt, as non-English speaking visitors often can’t, or choose not to, participate in our regular surveys. But we also periodically organise volunteers to ask all visitors as they enter the museums for their postcode or country of origin to try and get more even data; Wi-Fi sign-ups are also a useful source of country of origin data.
Postcodes also give us great information on things like realistic drive times, and if we’ve done a poster or leaflet campaign, where these have been most effective – once we know that we can start to ask ourselves why. Postcodes, of course, also allow us to do segmentation.
Segmentation tools basically categorise populations into demographic types by analysing significant social factors and behaviours – individual postcodes are classified based on the dominant features that characterise the population of the area. This, of course, isn’t an exact science, and we all know that we aren’t identical to our neighbours, but since we aren’t trying to understand individual people, but rather generalise about our audience as a whole, it is an extremely useful tool.
I use a number of different segmentation tools to understand our audiences, most often ACORN, English Indices of Deprivation and Audience Spectrum.
ACORN is the tool used by the University’s Widening Participation Office to understand who it is engaging with, so by using ACORN we can ensure consistency and comparability with data collected by the wider university. ACORN divides the population into five top level categories, within which there are 17 groups and 56 types (excluding non-residential properties). On the top level ACORN categories are:
Affluent Achievers are some of the most successful people in the UK. Middle aged or older people, the ‘baby boomer’ generation, predominate with empty nesters and wealthy retired. They tend to be well educated and have managerial or other professional occupations with salaries above the national average. Confident with new technologies and managing their finances, these are healthy, wealthy and confident consumers.
Rising Prosperity are generall younger, well educated and mostly prosperous living in major towns and cities. They are often yet to start a family or have young children, and are highly educated younger professionals moving up the career ladder. They are the internet generation and are confident with technology, engaging online. They have a cosmopolitan outlook and like to eat out in restaurants, go to the theatre and cinema, and make the most of the culture and nightlife of the city.
Comfortable Communities includes much of middle-of-the-road Britain, whether in the suburbs, smaller towns or the countryside, and all life stages are represented. These are stable families, empty nesters, comfortably off pensioners and young couples just starting out on their lives together. Generally employed in professional, managerial, clerical or skilled occupations, their income and education levels are around the national average. Less technologically engaged than other groups, they are often interested in activities such as gardening, cooking and photography.
Financially Stretched includes both low income renters, including those in social housing, as well as term time student residents. For the former, income tends to be well below the average and they work in administrative, clerical, semi-skilled and manual jobs. Apprenticeships and O Levels are the most likely educational qualification, and unemployment is above average. Overall, while many people in this category are just getting by with modest lifestyles, a significant minority are experiencing some degree of financial pressure. The students within this category are likely to live in halls of residence, flats or shared houses and have little in the way of expendable income. These young people spend a significant amount of time online and have active social lives.
Urban Adverity live in some of the most deproved areas in towns and cities across the UK, household incomes are low, people struggle with debt, and levels of qualification are low. Those in work are likely to be employed in semi-skilled or unskilled occupations. They live modest lifestyles, with their entertainment spending being concentrated on activities in the hime.
We graph the ACORN distribution of all our UK visited, which is interesting, but of there aren’t an even number of people in each of the ACORN categories, and differnt areas of the country have a different population composition, so what I find more useful is to compare the ACORN compsoition of our Oxfordshire based audience, with the ACORN composition of the Oxfordshire population. We can expect the county to be our ‘constituency’ or ‘catchment area’, so people who we can reasonably expect to visit use semi-regularly, disparities between the compsoition of the Oxfordshire population and our Oxfordshire audience can be telling.
Indices of Multiple Deprivation
Another segmentation tool that I have started using recently are the English Indices of Multiple Deprivation. This is a UK government framework which scores areas based on: income, employment, health deprivation and disability, education skills and training, barriers to housing and services, crime, and living environment. These are combined to give an overall index of multiple deprivation given as a decile: 1 indicates the area is in the most deprived decile, and 10 the least deprived.
This is similar to ACORN in some ways because it is socio-economic, but unlike ACORN segmentation which requires a subscriptions, there are free tools available to analyse posctodes on the basis of indices of deprivation (the one I use most). Also, the “LSOAs” (Layer Super Output Areas) that are used are clear geographical areas which I can look at on an individual basis, so it not only tells me that I am not reaching a certain type of person, but also if we are struggling with a specific geographic area (and the profile of that area). That can provide valuable insight into where we should be raising awareness of our services through marketing and community outreach.
The final postcide segmentation tool that we use that I am going to mention is Audience Spectrum. This is a service provided by the Audience Agency, that we are required to use as an Arts Council Funded organisation. Rather than segmenting audiences on socio-economic factors, Audience Spectrum, again using postcodes, segments audiences on the basis of their attitude towards culture, and by what they like to see and do. This provides a different perspective on who we are engaging.
Aucdience Spectrum breaks the population into ten different categories, and while we can’t look at the composition of our area, we can compare the breakdown of people who engage with us with people who enagage with other participating cultural venues in our region (the South East) that also participate in the programme, and with the breakdown of people engaging with cultural venues nationally.
The groups are:
So at the start of 2017 I pulled together all the data we collected over 2016, and I did a number of things with it, but one thing I did was pull together postcode and country of origin data from all our sources and used it as one way to provide a profile of who visits us. I compared this with the local population and who visits other cultural organisation to identify areas for attention, opportunity or improvement, and compared it with year on year data to see how our audience is changing, and facilitate us asking the question why.
This is only a small aspect of our audience evaluation work, but I hope some others have found this interesting!