Bobbi Nance, Park District of Oak Park, Senior Manager of Strategy & Innovation
As much as we hate to admit it, we humans are a fairly predictable bunch. Our habits, routines, and preferences remain the same until an external circumstance changes or life event such as moving, getting married, changing jobs, etc. occurs. (If you doubt me, I would recommend Charles Dugg’s book “The Power of Habit” to learn why).
This is why in job interviews, interviewers usually ask a few “Tell me about a time when…” questions. They are trying to learn about past behaviors in order to gauge potential for future success. Unfortunately, these questions sometimes fail to predict a new employee’s performance for a couple of reasons:
- Asking only 2-3 of these types of questions bring relatively few data points when compared with the whole of an interviewee’s prior work experience,
- The interviewee may not be answering in a completely truthful manner or it may be biased to shine a more positive light on the individual, and
- The circumstances which allowed the interviewee to be successful in the past might not be present in the new role/company.
It may be hard to predict the future behavior of a new employee based on a few interviews, the circumstances change dramatically when we look at customer data. We have years of data to pull from, and assuming it was collected properly, we can be assured that it’s accurate and unbiased. While a portion of our customer base is always in flux, the majority of our customers will use our services similarly from week to week.
The Netflix Story
We’ve heard stories about big companies using data to advertise, cater, and sell to their customers. One of my favorite examples comes from Netflix. Although I’ve never spoken with, met, or e-mailed a Netflix employee, the company knows me through the content I access. They know what I like to watch, movies that I finish in one sitting, and which movies I turn off halfway through.
Through their data, Netflix knew that they had a large group of customers that:
- Watched “The Social Network” from beginning to end,
- Liked movies with Kevin Spacey, and
- Liked the British version of “House of Cards.”
When talk of an American version of “House of Cards” starring Kevin Spacey and directed by David Fincher (who also directed “The Social Network”) started, Netflix found themselves in a Venn diagram with the new show and a bunch of $$$ in the middle and jumped on the opportunity. Based on the subsequent success of House of Cards and others, Netflix’s business model has expanded to include creating much more new original content, most of which they’ve used data to figure out what their customers wanted even before their customers knew they wanted it. Although there is never be a sure bet, data creates safer bets.
Netflix and local government operate in two different spheres but data is a part of both spheres. Local governments use some of the same methods used by Netflix to predict and provide better services to resident. Local government is providing a new source of entertainment; we have the potential to change lives.
In parks and recreation, we market a new program to customers who have signed up for a similar program. For example, let’s pretend that we’re offering a new one-day class on a Tuesday night costing $40 where participants will have the chance to construct and take home their own terrarium. This class would require advanced registration to make sure the right amount of supplies. Normally, most agencies would send a mass email to past participants of nature-based programs and hope for the best. However, most park and recreation departments have much more data available to them that is going unused.
What if instead, they examined past participant data to find out which customers have registered in advance for a one-day nature based class, costing $30 to $50, scheduled on a weeknight? It would narrow down the marketing list. This customization would allow the department to mail 75 postcards instead of 1,000 emails which would most likely result in a higher registration rate. Better yet, what if they looked at their customer data in advance of creating the program and built a series of classes around the most popular registration method, topic, day of the week, price, etc.?
Other examples include:
- Using customer transaction data to optimize in-person customer service hours and/or online chat availability,
- Using timeclock data to predict which employees are on track to cross certain thresholds such as overtime or annual limits where pensions and health care would have to be provided (not simply to withhold benefits, but to ensure that they are offered deliberatively and budgeted for),
- Analyzing customer demographics to optimize marketing efforts for reaching out to new customers,
- Analyzing daily visits to a swimming pool versus the daily weather to get a better prediction of how many people will come through the door on any given day based on temperature, humidity, precipitation, etc.
- Analyzing accident/incident patterns to steer the audiences and topics of additional staff safety training,
- Using beacons or other smart devices to monitor traffic in a park or public space to optimize security, trash removal, park maintenance, most popular features, etc., and
- Monitoring customer restaurant reviews to predict where food sanitation violations are likely to be occurring.
These aren’t hypothetical scenarios; many are in place at my own agency. I see many of my peers missing out on these opportunities because of misunderstandings or incorrect assumptions. I have the following advice for local governments:
Industry benchmarks may not align with your current agency priorities. In order for industry-wide benchmarks to exist, they have to be generic to apply to an entire industry, usually over a long period of time. Benchmarks serve as a good starting point and inspiration for improvement, and they may be helpful as an annual spot-check in how your agency is doing, but they may not drive the specific change that your community needs. What you measure matters, you need access to continuous data focused on the areas of desired change, not a once-a-year comparison report.
Data transparency does not equal data analysis. I’m all for government transparency efforts. My own agency includes live results of our performance measures on our website for our community. However, putting it out there doesn’t mean that:
- anyone is looking at it (or that it’s made to be easily found and accessed)
- people looking at it can understand what it is
- it’s helpful to the community, or
- that it shows what’s both good and bad about agency performance, i.e. sharing data in an effort to actually be good, not just look good.
If it’s not useful, it doesn’t get used. This sounds simple, but ultimately, it’s one of the keys to success. Just as reports are created, presented, and then immediately shelved, the key to data efforts is to make sure that it’s easily accessible. If your agency has a set of organizational performance measures complete with dashboards, this will drive decision-making at the organizational level. If you want your supervisors and front-line staff to make data-driven decisions, you have to provide them with the specific data.
Taking advantage of data does not require an on-site data scientist and expensive tech tools. Having these things would be nice, they are not required. Many agencies assume that it’s all or nothing, instead of making a concerted effort to do what they can, starting with what they have. The cost for storing data continues to decrease as technology increases, making data analysis tools surprisingly reasonably priced.
If your agency is becoming more data driven and wants the ability to take full advantage of what’s possible (even if not now, but somewhere down the road), I would encourage you to consider specific goals in relation to these four points.
For example, if you are setting performance measures, think about how those would be applied, accessed, and used by individual areas/facilities/supervisors, not just how you’ll be reporting them. If you’re thinking of purchasing dashboard software or other technology to display and monitor your data efforts, select one that allows you to customize your measures to those that reflect your current agency strategic plans and goals and can adapt to future ones, not just pre-set industry benchmarks, or is limited to one area such as operational dashboards or financial dashboards.
If you’re interested in learning more about my experience in leading data efforts at my organization, including the tools we use, how we rolled it out, and how we were able to use data to shift our organizational culture, here’s the recording to a webinar that I recently offered in partnership with iDashboards. Also, feel free to check out some of my other blog posts and resources related to the same subject:
- Data is not a Four Letter Word
- This One’s for the “People Persons”
- Growing Data at your Organization
- A Lesson in Government Transparency from an Unlikely Source
Also, a quick little plug – if you are someone at your organization is interested in learning more, it would be fantastic to see some fellow ELGL members at the following educational and networking events in the next two months:
- Do Good Data Conference – Data conference attracting people working in government, nonprofits, and foundations from all over the world (Chicago at the end of April)
- Data Driven Government and Parks & Recreation – National Park & Recreation Association’s next Innovation Lab (Boston in mid-May)