Pros and cons of activity data

Activity data allows you to monitor how your customers interact with your business, brand, products and services over time.

The main advantages of activity data are:

• It allows you to see what your customers actually do as opposed to what they say they do. This can provide useful insights on product development or service improvement.
• It is never ending because human beings are in constant activity so it provides a rich vein of insight.
• It is often self-generating. As long as you have the infrastructure set up to collect the data it will be collected in the normal course of everyday business.

The main disadvantages of activity data are:

• The sheer amount of potential activity data that you can collect is staggering – even in a small business. It can therefore be confusing as to what to collect and why. Focusing on your strategic questions (Chapter 11) can help you concentrate on the data that’s most helpful for your business.
• The majority of activity data is unstructured, and this can potentially be harder to analyse and draw insights from.

Activity data around your website (such as Google Analytics) lets you know how many visitors your site gets, where they go and where they lose interest and jump out. This information can therefore help you to revise or update your website to make it more sticky (meaning that visitors stick around on your website for longer) and provide more of what your customers’ actions indicate they want.

Using activity data

The applications for activity data are as endless as the activities about which data can be collected and analysed.

Activity data tells you what people are doing and these insights can help you improve everything from sales to website traffic to your understanding of aspects of human experience, such as sleep and exercise.

For example, Jawbone, the company that makes the activity band, now collects health, movement and sleep data from millions of people around the world (including me). This means that Jawbone has access to 60 years’ worth of sleep data – every night! No company has ever had so much sleep data, which means that Jawbone is able to analyse the data to understand more about sleep, sleeping patterns and what disrupts those patterns. Jawbone could, for instance, look at the data and work out how many hours of sleep are lost, on average, when the Super Bowl is broadcast in the US or how long it normally takes for business travellers to return to normal sleeping patterns after international travel.

Getting your hands on activity data

How best to get your hands on activity data depends on what activity you seek to understand and whether that data currently exists or could exist in your business.

You could ask your target audience questions regarding the activity you want to understand more about. This can be done through a market research survey or focus group. Or you could put a mechanism in place to capture their answers as part of the activity in question. For example, if you want to know more about why someone bought a particular product online, you could add the question to the checkout page so he needs to answer it to complete the purchase process.

Activity data that most businesses already have is transaction data – although it may not always be digital or datafied. Depending on what strategic questions you’re seeking answers to (see Chapter 11), it may be worth putting this data on computer so you can run analysis on the data. You could, for example, identify which period of the day or week is the busiest for online orders.

Pros and cons of conversation data

Conversation data can be extremely useful for small business owners because it provides insights into how happy or otherwise your customers, clients and suppliers are.

The main advantages of conversation data for small business are:

• It provides you with real-time access to customers and what those customers think and feel about your products and services.
• It allows you to react quickly to changing customer requirements and improve customer service.
• It allows you to handle customer upset more quickly so that the upset or dissatisfaction doesn’t escalate and damage the business or the brand.

The main disadvantages of conversation data for small business are:

• Most conversation data is unstructured, which can make it more difficult to analyse.
• The vast amount of conversation data in any business can be potentially confusing with regard to what conversation data is the best to collect and analyse.
• Legal ramifications affect recording conversations, and you need to be aware of these in your country.

Legally, in most parts of the world, you can’t just record customer or client conversations willy-nilly – the recording must be relevant to your business. Additionally, you must make every effort to inform those parties that the conversation is being recorded and give them the opportunity to opt out.

Using conversation data

Conversation data allows you to know what is being said about your business in conversations between your business and your customers, clients and suppliers.

This information can shed valuable light on shifting buying patterns and changing requirements, highlight service or performance issues in the business or indicate new product or service development ideas.

Getting your hands on conversation data

Wherever you are currently having conversations there is an opportunity to collect conversation data.

If you operate a telephone sales department or customer service department where customers call in to purchase or follow up on order delivery then you could record those conversations. Text-based conversation data also exists in the emails you receive from your customers and in any conversations that take place on any of your social media platforms.

Clearly social media platforms are critical for conversation data, and they provide a wealth of information that can then be analysed for future improvements. A good way to start might be some simple sentiment analysis (perhaps using the software mentioned in this chapter) to find out what your customers are saying about your product or service online.

Making one person or team responsible for communicating with your customers online and quickly responding to any complaints or issues is a good idea. If you don’t yet have active social media platforms then you need to create those as the first step. Without them, you’re missing out on a wealth of data.

Pros and cons of photo and video image data

The main advantages of photo and video image data for small business are:

• The data may already be collected via security footage, and finding better ways to use that data may not be very expensive.
• Photo data contains embedded information such as when the picture was taken. GPS (global positioning system) tracks where it was taken too.
• Photo and video data can be processed using face- and shape-recognition software.

The main disadvantages of photo and video image data for small business are:

• Video footage alone can create huge files, so it’s important you have a defined and relevant purpose for collecting and storing the data.
• Photo and video data can be used to recognise individuals without them knowing, causing a concern about invasion of privacy.

Using photo and video image data

The applications for photo and video image data are endless and they already impact crime prevention and health – as well as business.

Photo and video image data can be extremely useful for small business owners because it provides insights into what people – especially in a retail environment – are actually doing.

If your business doesn’t have retail premises, then the applications for video data may be limited for you. Photos can provide useful information for many different types of businesses – photo metadata, for example, tells you who took the picture and when and where it was taken. But brands can now use photos and video data to detect where it is used. For example, a clothing brand can start searching for photos that show someone wearing its brand logo or restaurants can search for photographs taken inside or outside their premises. This allows them to identify and potentially engage those individuals.

In 2013, typhoon Hiayan hit the Philippines and killed over 6,000 people and damaged or destroyed over 1.1 million homes within a matter of hours. Almost immediately, a team of UK volunteers started to create a vital map of the damaged areas using just social media – drawing largely on photo and video image data. In today’s world, people share their experiences as they’re happening in almost real time, so photos, tweets (#Hiayan) and video about the disaster were posted on social media at a rate of about a million a day! Those million data points were then filtered using artificial intelligence to pick out the ones that could be important. The team of volunteers then made an assessment of what they saw. For example, in a photograph they would ask, ‘How much damage is visible?’ and they simply needed to click the appropriate button: ‘none’, ‘mild’ or ‘severe’. For text-based messages such as tweets or Facebook updates, the volunteer was asked to decide if the text was ‘not relevant’, ‘request for help’, ‘infrastructure damage’, ‘population displacement’, ‘relevant but other’ and so on. Each piece of data (picture, video or message) was then assessed by between three to five different people to make sure the assessment was consistently and therefore probably accurate.

By pinpointing where the data was coming from in the Philippines (using GPS sensors in the photos or through the text) the work of the volunteers then creates an online map, not just of the disaster zone but of the needs in each area.

The result was that when the disaster relief effort arrived in the Philippines they didn’t need to waste days working out what was happening and where the worst hit areas were. From the map created by people halfway around the world, relief providers already know who needed water, who needed food, where the dead bodies were, where people had been displaced, where the most damage was and what hospitals were least damaged and therefore more able to help the injured.

Getting your hands on photo and video image data

Video and photo data can be obtained by simply starting to collect it using digital cameras.

In many cases, companies are already using video recording for security reasons. If you’re already doing this, consider whether that video data can be used and trailed for analysis.

If more advanced analytics are required, it often means installing purpose-built systems. For example, if a small shop already has a network of CCTV cameras installed then the data can be brought together for an analysis of how customers walk through the shop, where they stop and which parts they avoid. Testing the existing data shows any gaps where new cameras or systems need to be added to improve the analysis.

Photos can often be collected and analysed from the vast amount of photos shared on social media sites such as Twitter, Facebook, Pinterest and many others.

Pros and cons of sensor data

Sensor data can be extremely useful for small business owners because it can help to answer some important strategic questions. For information on how to identify your key strategic questions, see Chapter 11.

The main advantages of sensor data for small businesses are:

• It doesn’t have to be expensive and can provide really useful insights for increasingly sales and influence.
• Once installed, the data is self-generating.
• Sensor data can radically improve productivity and maintenance when installed.
• Many devices, such as smartphones, already contain very sophisticated, ready-to-use sensors.

The main disadvantages of sensor data for small business are:

• You may not have access to sensor data.
• Sensor data often lacks context and only measures a very small part of reality.
• Sensor data most likely needs to be combined with another dataset (such as transaction data) to get the best results.

I worked with a small fashion retail company that wanted to increase sales but had no data other than its traditional sales data. After establishing what questions it needed answers to, we installed a small, discreet device into the shop windows that tracked mobile phone signals as people walked past the shop. Everyone, at least everyone passing these particular stores, had a mobile phone which the sensor in the device would pick up and count. The sensors would also measure how many people stopped to look at the window and for how long and how many people then walked into the store. Sales data would then record who actually bought something. By combing the data from inexpensive, readily available sensors placed in the window with transaction data, we were able to measure conversion ratio and test window displays and various offers to see which ones increased conversion rate – the conversion of customers’ money to the store’s coffers.

Not only did this fashion retailer massively increase sales by getting smart about its data requirements and combing small traditional data with untraditional big data, it used the insights to make a significant saving by closing one of the stores. The sensors were able to tell them the store owner that the foot traffic reported by the market research company prior to opening in that location was wrong and the passing traffic was insufficient to justify keeping the store open.

Using sensor data

Increasingly, more and more machines are equipped with sensors to monitor performance and provide information on when best to service or repair the machines.

For example, modern cars are full of sensors that measure everything from fuel consumption to engine performance. These sensors allow for dynamic servicing and better long-term performance. On-board sensors also alert the driver if he gets too close to another car or object and can even parallel park the car without the driver doing anything!

In retail, data has long been collected via bar code – however, the sensors known as radio frequency identification (RFID) systems increasingly used by retailers and others are generating 100 to 1,000 times more data than the conventional bar code system.

Rolls-Royce is one manufacturer that has transformed its business through sensor data. You may not realise it, but Rolls-Royce manufactures nearly half the world’s passenger jet engines, including the Trent 1000 – the engine that powers many transatlantic flights. When in operation, these engines reach incredibly high temperatures – half the temperature of the surface of the sun and 200 degrees above the temperature at which the metal used to make the engine melts! The only reason it doesn’t melt is because the engines are cooled through special passageways and channels that keep the heat away from the metal. Needless to say, it’s vital to know that everything is working and doing its job! The engine is full of vital components all engineered with absolute precision, including an on-board computer that collects and monitors data from sensors buried deep within the engine, measuring 40 parameters 40 times per second, including temperatures, pressures and turbine speeds.

All the measurements are stored in the computer and streamed via satellite back to Rolls-Royce headquarters (HQ) in Derby, England. And that’s true for the entire fleet of Rolls-Royce engines – which is a lot of data when you consider that a Rolls-Royce-powered engine takes off or lands somewhere in the world every two and a half seconds.

Whenever those thousands of engines are in the air, they’re gathering data that’s constantly sent back to HQ and constantly monitored using clever data analytics that look for anything unusual occurring in the engine or any sign that it may need to be serviced early or repaired. In Derby, computers then sift through the data to look for anomalies. If any are found, they’re immediately flagged and a human being checks the results and, if necessary, telephones the airline and works out what needs to be done – often before the issue escalates into an actual problem.

These sensors therefore allow for dynamic maintenance based on actual engine-by-engine performance rather than some automatic rota system based on time alone. Instead of pulling an expensive piece of equipment out of service every three or six months, these sensors allow the airlines to maintain their fleet much more cost effectively, and, more importantly, these sensors make the planes much safer.

A similar (if smaller scale!) application could benefit your business. For example, a haulage company could use sensors to monitor vehicle wear-and-tear, thereby maintaining its fleet in the most efficient way. Alternatively, a manufacturing company may use sensor data to monitor machinery on its assembly lines, identifying potential faults before they occur.

Getting your hands on sensor data

Sensor data may be accessed either by using sensors inbuilt in devices you already have or by installing new sensors.

The world is filling up with sensors. Smartphones can contain GPS sensors, accelerometer sensors, light sensors, fingerprint readers and so on, all readily used by any small company. Smart watches contain all these features as well as heart-rate sensors. Soon, most devices, from diapers to clothing, sports equipment to cars and thermostats, will generate sensor data you can use for business purposes.

Sensor technology is developing very rapidly, so often these sensors, like the ones I used with the fashion retailer (see ‘Pros and cons of sensor data’ earlier in the chapter), are significantly less expensive than people may imagine.