Master Buyer Behaviors with higher quality data

Master Buyers Behaviors with Higher Data Quality

10/9/2017

The idea that the customer is in control when it comes to the modern buying paradigm just gets stronger. Today's buyer often kicks off the process of considering a purchase with a web search to gather basic information on a product or service. As their knowledge and interest continues to grow, they often follow that initial action up by researching reviews put out by peers, checking social media for advice, and in some cases visiting forums to submit questions. All of this can happen quickly and almost always occurs before they ever reach out to, or look at your company, directly.

What are today's marketers doing to keep up with and out-pace the competition? Market leaders have championed the idea of utilizing "Big Data" to drive marketing campaigns and initiatives to better identify potential patterns and consumer behaviors, and then react to them. Big Data refers to the collection of large quantities data, from multiple sources that are often too complex for simple reporting tools like excel spreadsheet, dashboards, and website analytical tools to convert into meaningful insights.

"Big Data" is so "big" that it: (1) requires more than one server, (2) is too unstructured (such as text, images, and video) for a row-and-column database, and (3) is too continuously flowing for a static data warehouse.

Even if you have the capabilities to capture these types of data, if you don’t have the resources or skill sets in place to convert the data into meaningful insights then you could become overwhelmed by the sheer volume of information. For example, many organizations that employ the use of Big Data in their marketing strategies utilize what we might call a “Data Scientist” whose primary focus is to make sense of it all and produces the applications and models in order to present the data.

For many companies, this really isn't a practical option. Although the potential with all this information is great, at first it is often prudent to start small and get the basics down before graduating to more complex analytics.

Try starting your efforts by focusing on the organization of your own database and making sure that there is one united “data truth” across the entire company. Begin by looking at your CRM system or data repository and addressing these two major factors:

Data Hygiene – This one is somewhat simple, yet it can easily be overlooked (and in many companies often is). If the data you’re using isn’t up to date (at least as much as it can be) and/or it isn’t as accurate as possible then you may not be reaching your best prospects, regardless of how well thought out/executed your campaigns may be. A good place to start is by considering the value of services like InsideView that specialize in cleaning and supporting data can bring to an organization.
Data Segmentation – Applying segmentation to your data allows you to not only target unique groups with tailored messages, but also gain insights into that specific group’s behavior, affording you the opportunity to continue to adjust and evolve your message.

Creating these targeted messages requires you to speak to the very specific pain points and needs that your potential customers may have. One-size does not fit all in this area and these issues will be different things for different groups. Have you analyzed your current database and organized its content into list segments or by target groups? If not, then this should be your first analytical task. In the modern multi-channel, fast paced, competitive and content driven market place, only hyper-focused and targeted messages break through all the noise.

Cleaning and segmenting your data is the foundation of a good and effective marketing practice, it allows companies to reach the right people (target prospects), with the right message (speaking to unique pain points), and at the right time (when they are researching). Ultimately, this leads to growth in your “best prospect” pool which results in greater sales.