When it comes to using Big Data for marketing, there are three core elements involved: volume, velocity and variety. Through the following lines, I will teach you how increasing volumes of big data can affect your marketing strategy.
First off, you will notice that Volume, which is the amount of data, is easier to relate to Big Data. According to the 2013 number, the volume of data is estimated at 2.5 zettabytes, but it is expected to reach 8.9 zettabytes by 2018. Despite the number being large, the term ‘big volume’ is more of a qualitative term than a quantitative one.
While to an ant I may look like a giant, I am far from being one. Similarly, what may be considered as high volume differs from industry to industry. This is why the physical size of the data is not as important as the ability to manipulate and utilize high volumes of data in your business.
The question is: In terms of data types used by small to medium, what does the volume element of Big Data entail?
Before answering that question, let’s take a look at the types of data used by SMEs and then determine and see if they are affected by ‘big volume’.
- Quantitative Data – Data based on customers’ direct interactions with your business, which is usually collected through means like purchase history, web analytics, an optimized landing page, and social media interactions, are easily available. In most cases, it can be integrated into customers’ existing view and their activity. However, again, this doesn’t increase the data volume for most SMEs.
- Identity Data – Identity data allows businesses to individually identify an existing or potential customer. Though the various routes customers use to interact through can impact it, it is not a main reason of higher data volume in SMEs.
- Descriptive Data - Additional client information helps you understand who they are as well as their age group. This type of info consists of data like family details (e.g. marital status), lifestyle related details (e.g. car type) and profession-related data (e.g. education level). This information may not always be available, but it can be collected through third party data or other data capture methods. However, though this adds to the size of your data, it hardly is used to determine the scale of Big Data.
These are the three types of data that businesses rely on despite being in a combined customer view only a small amount of the time. They are stored in a structured, accessible and usable way to allow existing tools to analyze and utilize the data to deliver a plan for action. With Big Data, most of the new information you need will come from the data tiers following the aforementioned three.
- Qualitative Data - This type of data further determines prospective client behavior and comprises attitudinal information, opinion and motivational indicators. While a large percentage of this data is collected through structured surveys, most of it will be part of email conversations, unorganized social data, and customer service text. Qualitative data, particularly of the social type, is becoming very popular. On average, 6,000 tweets per second are generated according to stats from August 2013. By utilizing this data, companies can come up with strategies that ensure higher conversion rates.
- Machine Data – Due to enhanced connectivity thanks to the web, large amounts of data from devices, apps, etc. track and save details of all activity in a more detailed way.
After going through the final two types of data, you must be wondering what their value is for your business. There are mainly three.
1) Enhanced Customer Engagement
By using Social Commentary, you can better understand your customers’ view/perception. This feat can be impossible via the standard layers of data and customer surveys may not provide you with the increased conversions you want. However, Social Listening offers you access to present and past customers’ opinions and feelings. As a result, you’ll be able to understand your clients better and ultimately improve the level of engagement with your customer.
What you will need to do is analyze unstructured data to discover important key phrases. Think of it as a word cloud where you gather common words in a body of text. To give you an example on this, check out the picture below for a word cloud detailing the key messages of a major cruise company’s website.
This simple approach and does not create relationships between words. This is why, when used with other techniques, it will give you insight into your customers’ conversations.
2) Purchaser vs. Consumer
One thing businesses should know is that the products they sell are not necessarily used by their buyer. When it comes to traditional data, the purchaser is the client you address your marketing efforts towards. With the arrival of machine and social data, three main groups of buyers have been identified.
- Buyers who use a product.
- Those who purchase the product as a gift.
- Consumers who actively use the product but have not purchased the product
To explain this further, take LEGO for example. While the main consumers of the product are kids between 3 and 12, parents and grandparents are those who purchase LEGO sets. With the help of survey type information and programs like LEGO Club Membership, the company has been able to form relationships with their actual consumer (user). However, these rely on the active engagement between children and their products.
When I was younger, every time I played with a toy I enjoyed, I would tell my friends in the playground. Things are different now as the playground is more virtual than physical and social media are the means of communication. Due to this change, LEGO can easily understand the usage of their merchandise via direct interactions and social engagement. As a result, improved and personalized communications is achievable at the buyer’s end as well as the consumer’s. This explains LEGO’s child-friendly social website My LEGO, which targets children to provide them with the full LEGO experience rather than forcing them to go for 3rd party social media platforms.
3) Better Understanding
As the depth and breadth of data increases, you can easily generate statistically significant models of infrequent but highly predictive factors. Since data can be segmented according to smaller groups of customers, you can handle one-to-one conversations better since you have full access to recent and past information. For example, using the location information collected through smartphones and apps, you will be able to target your clientele based on standard and abnormal activity.
Gathering and using Machine data allows you to take this step further as you will start seeing info that allow you to predict future events before your clients can realize their needs. For example, by collecting data about your customers’ cars, you can tend to needs long before they show up. On the other hand, you can change how a product is used due to a change in circumstance or ownership or because of a potential future shift to another new product.
Now that you have reached this line, you fully understand how higher data Volume adds breadth and depth to your level of understanding of your clientele.