People Data

Data driven companies look at a broad range of information to optimize everything from their business processes to their digital marketing. They use people data to gain an understanding of their audience and how best to segment them in a digital age. When you use this data in your digital marketing efforts, you create a more relevant and personalized experience. Janrain found 85 percent of companies used personalization techniques to improve the customer experience. You can use people data to differentiate yourself from your competitors, but you need to understand how to effectively implement this data into your digital marketing campaigns.

Context from Identity vs. Attributes

Context matters significantly in your marketing efforts, and you gain many contextual clues from a person’s identity versus their attributes. Someone’s identity is who they are, and this remains consistent throughout the marketing process. The person’s identity never changes, but their attributes change based on context, such as going from the office to their home or switching from a desktop channel to a mobile channel. When you know the buyer’s context, you can optimize your marketing strategies.

Difference Between B2B and B2C Data

People data in B2B and B2C marketing display several differences. Buyer motivation is one significant area where B2C and B2B data differs. Hubspot found B2B buyers focus on efficiency and company expertise, while B2C buyers are on the lookout for deals. You have a longer buying cycle for most B2B purchasing decisions, and you’re rarely dealing with only one decision maker. Your B2C and B2B marketing efforts both benefit from people data, but you approach each market in very different ways.

Personas and Audience Segments

People data helps you identify audience segments and create buyer personas. While these two terms are sometimes used interchangeably, they represent two distinct concepts. An audience segment describes a group of potential customers you focus on, such as Chief Information Officers at technology startup companies. You identify broad characteristics of this market, such as their general demographics and pain points. You create buyer personas to make an in depth profile of people or groups that are close to making a purchase decision. You look at the challenges they face, the customer’s story and pain points unique to that persona. Typically, you lean on audience segments for your top of funnel marketing efforts and deploy buyer personas for the middle and end stages.

When you effectively implement people data into your marketing efforts, you can use a data driven approach to pinpoint what your customers want. As content personalization becomes the default expectation for buyers, you need to put your people data to good use in order to better serve your clients and your competitive positioning.

Sources:

http://www1.janrain.com/rs/janrain/images/Industry-Research-Unlock-Customer-Data.pdf

http://blog.hubspot.com/agency/differences-b2c-b2b-marketing

Unstructured, Structured and Standardized Data

Have you ever accidentally put an object in the wrong place and get frustrated when you can’t find the object later on? The function of data repositories and information systems, are to store large quantities of data in the right place, so it can be easily accessed at a later date. The aim is to make the formatting consistent across systems so that different types of data are compatible with each other. Usually, systems will have data models in place that lay out the structure, manipulation and other aspects of the stored data in order to define and format the data to make it more accessible. These types of data are usually separated into three categories: structured, unstructured and standardized.

Structured Data

Structured data is the easiest type to capture and organize in a data model. This is data that lives in a fixed field within a file, like information you would find in a spreadsheet. In order to store structured data, you have to define which fields of data you are planning to store and organize it into rows and columns. An example of this is keeping a database of clients along with names, dates, addresses, currency and so forth. It also may involve restricting the data input in certain ways, such as number of characters or drop-down menus.

The advantage of structured data is that it can be easily entered, stored, searched and analyzed. Data like this is usually managed using a Structured Query Language (SQL). When data isn’t properly structured, it can mean that you’re missing out on some big marketing and advertising opportunities – which we’ll get to in a minute.

Unstructured Data

Unstructured data refers to the information that doesn’t live in straightforward rows and columns. This information is a little more complex and can’t usually be stored in tidy fields. It includes things like email messages, text documents, social posts, photos, videos, web pages and other kinds of documents. The information within these files might be organized, but the data still doesn’t fit into a database in a logical way. Studies have shown that between 80 to 90 percent of data in any company is unstructured – and this number is continuing to grow!

Because so much of every company’s data tends to be unstructured, it makes sense that businesses want to find ways to use this information to make better decisions. The issue is that it’s hard to analyze unstructured data. That’s why a number of different software solutions have been developed in order to try to sort through unstructured data in order to find important information that businesses can use in order to stay competitive.

Standardized Data

Standardized data is data that has been received in various formats and then transformed to a common format that makes it easier to compare the two. This helps make many types of data crisp, clear and easily accessible. For instance, if you have a field for street names, and some people write North Main Street while others write N. Main St., you could standardize the fields to be N. or S. and Street or St. It’s a way of making the data fit more neatly into fields so that search queries can be run more effectively.

Why Are They Important?

Businesses in every industry are scrambling to compile and measure more and more data every day. Structured data can offer vital information about customer interactions and consumer behavior that can help translate into increased sales if dealt with in the right way. Processing unstructured data is, arguably, even more important, but it does present some challenges. Text-based information sources like email, social media interactions and mobile data can often go completely ignored because it’s difficult to compile and analyze. However, this unstructured data can tell us a lot about the customer journey and give us powerful insights into buying habits.

How to Analyze Unstructured Data

There are a number of different, non-traditional ways to analyze unstructured data. Companies can review conversations on social media and analyze customer interactions in call centers or in-house, and then find ways to record them in a structured format. You can also set up automated processes to capture data, such as tools that monitor social media sites or RSS feeds. Figure out which areas are of interest to your customers, and organize this information into relevant categories. In this way, you can work toward standardizing the data to make it easy to compare it with structured data to identify trends and patterns.

What Does This Mean for Marketing?

Social data is powerful for driving your content marketing strategy, as it helps you understand what content to create and how much weight to give each type of content within your plan. You can use metrics from Facebook Insights, for example, to see how well different types of posts are performing. Other tools, like Optimal Social, can offer structured data on what types of content your readers are engaging with.

Another important use of structured data is to boost your search engine rankings. Web crawlers are now trolling sites to look for specific items and keywords, and also to verify that you have high-quality content on your site and are a company that deserves to pop up in user searches. This kind of rich data markup helps Google understand what your content means, not just what it is saying. Google analytics and other site analytics can help you optimize your site and your online content so that Google picks it up first.

About Diggen, Inc.

Diggen is a data marketplace to help marketers become and manage data driven enterprises. Data driven marketing initiatives accelerate growth, since it improves key performance indicator (KPI) metrics and increases revenue over 19%. However, marketers have monumental technical challenges accessing data assets, sourcing data providers, and integrating into their marketing technology stack.

 

Our platform uniquely combines a data marketplace to source all marketing data with middleware to integrate into all marketing technology stacks. For example use our intuitive web interface to append gender, age, location to email addresses and integrate into an email service provider. Therefore, marketers segment their audience for newsletter emails, which creates relevancy, more engagement, and increases conversions.

 

Visit Diggen at http://www.diggen.com.

 

Sources:

  1. http://www.innovatingstuff.com/2012/12/03/structured-and-standardized-data-sources/

  2. http://acumenmd.com/blog/human-condition-structured-unstructured-data/

  3. http://www.webopedia.com/TERM/S/structured_data.html

  4. http://www.webopedia.com/TERM/U/unstructured_data.html

  5. http://digitalmarketingmagazine.co.uk/digital-marketing-data/how-understanding-unstructured-data-is-useful-for-customer-insight/2198

http://www.npws.net/blog/how-structured-data-can-enhance-your-online-marketing

Better Data Drives Better Marketing- Importance of Master Repository

For the past few years, the term “big data” has been tossed around as cavalierly as the term “cloud”. Like actual clouds, the terms are more ethereal than substantial. Despite the imprecise use of the term big data, it is in fact quite real and of great importance. Because big data can be either structured or unstructured the need to organize it and integrate it are essential to capitalizing on it.

The three Vs; volume, velocity and variety are regularly used to describe different aspects of big data.

  • Volume- refers to the amount of data to be analyzed.
  • Velocity- is the measure of how quickly raw data enters the system.
  • Variety – Thanks to both its diverse sources, unstructured nature, and varied end uses the mixture of data can be untidy.

The cost, in time, manpower, and in building an expertise in managing big data can be overwhelming for some enterprises to manage in-house. Fortunately, outsourcing and Software as a Service (SaaS) are also viable alternatives. Cloud-based solutions have gone a long way toward making big data easier to manage, more accessible, and more secure.

The fact of the matter is the cloud in the context of big data storage is a real brick and mortar place. It is equipped with state of the art infrastructure and software making it far more secure and reliable than most on-site solutions.

Outmoded Solutions

In the past, a business’ data might be arbitrarily split between b2b data stored in a CRM (customer relationship management) system and customer (b2c) data relegated to a data warehouse. A significant shortcoming of this practice is that the absence of a complete picture of all of an enterprise’ data.

Realigning resources and centralizing data in a master location with mechanisms to log data will make it readily available in the future. Even though the freshly organized data can not impact the past it can serve as an invaluable resource for analyzing the future. Centralized data repositories do this by having all of the data organized and ready to be transferred directly into the enterprise’s marketing tools.

Master Data Management

A better way to store and analyze disparate data is to maintain it in a single location. Master data management ( MDM) allows administrators to streamline standards and tools across data sets. Thereby reducing costs and improving accessibility. Another significant advantage comes from the ability to eliminate inconsistencies, incorrect data, and duplicate information.

A single authoritative master source of data can eliminate erroneous customer contacts that result from data segmentation. For example a bank customer who has a checking account and a mortgage with the same

 

institution. Segmented data can cause the customer to receive a solicitation for mortgages based on their presence on the checking account data set. An effective MDM would be able to identify the multiple points of contact the customer has with the bank and prevent unnecessary customer contact.

An often overlooked consideration for centralized data repositories is their enhanced security and reliability. Thanks in no small part to economies of scale MDM is able to provide substantially improved system redundancy that extends well beyond simple backups and include greater cyber-security and alternative power sources. These enhancements go a long way to ensuring that a data is ready and available when it is needed.

Big Data is Not a Cure-All

Big data alone will not fix all of an enterprise’s ills, but it can go a long way to helping identify and correct shortcomings. Francis Bacon summed up the importance of big data when he said; “knowledge is power.” The best way to harness the power of big data is with the equally large solution of master data management. By creating a single authoritative view of data unwieldy and costly duplications are eliminated. MDM reduces the risk of loss of key data components that can occur as the quantity of data collected increases because of the superior hardware and software architecture afforded by master data management.

MDM creates an agile information environment that is capable of exploiting the volume, velocity and variety of big data. It supports strategic decisions by providing a 360-degree view of data. By obtaining a fuller picture of relationships, enterprises can effectively deliver custom content. A master data management system can’t promise success, but it can guarantee an enterprise the ability to succeed.

 

Personalization is The New Nirvana

Digital marketing, such as advertisements, we see are like works of art. Creative directors are tasked with composing a variety of elements from realism to sound composition, to developing an advertisement that will grab your attention, keeping your interest, instilling desire, and demanding action. The difference between art and advertising, is art is meant to be enjoyable, whereas an advertisement is meant to fulfill a monetary purpose. In today’s digital world, creating advertisements can get even more complex, such as adding in a layer of augmented reality, that will engulf the targeted customer into an entire experience. The problem creative directors have, is to build multiple works of art that will appeal to a variety of different personalities to drive the same end goal.

Using Data to Develop Marketing Content

True personalization is creating a group segment audience of 1. This is also known as ‘nirvana’. The challenge that presents itself here is that no two individuals are alike. Even the most extreme identical twins have a small degree of variation in their personalities. I want to talk about a concept that I like to call ‘Deep Data’. I want to differentiate this from Big Data, because I think Deep Data as a descriptor to determine a person’s psyche.

There are plenty of Big Data sources available to help creative directors build ads based on high-level segmentation. Understanding demographics, can help creative directors determine basic cultural differences, and develop advertisements that will fit in various markets. Where it starts to become tricky, is gathering the deep data sources. It’s much easier to figure out where someone lives vs. the emotional intelligence of that individual. The other challenge, is most people are willing to give up demographical information, but any info that would be seen as compromising to the person is much harder to attain.

This is where digital trust is crucial if we want to progress towards true personalization.

Gathering Deep Data

Between beacons, IP addresses, learning algorithms, cookies and surveys there are plenty of strategic ways to gather information about a person. The problem, is all this data gets collected and stored in various systems and there isn’t a current solution that combines them all into one. The other issue presented here, is tracking the success of individual campaigns. There are ways to track open rates, impressions, click through’s and conversions, but what this data doesn’t tell you, is why an ad didn’t work with those that didn’t convert, and why it worked with those that did.

As we move towards Nirvana, creative directors and marketers will need to be able to understand each individual and know exactly which mixtures of messages and content will create the efficacy that every business wants without losing the consumer’s trust.

References:

http://www.forbes.com/sites/sap/2015/07/11/marketing-nirvana-engaging-with-an-audience-of-one/

http://searchcontentmanagement.techtarget.com/feature/Location-data-adds-context-for-Web-personalization

http://www.forbes.com/sites/johnrampton/2015/09/03/better-data-enables-better-customer-segmentation/

http://www.martechadvisor.com/marketing-analytics/clickagy-launches-data-driven-content-providing-intelligent-on-page-optimization-using-audience-profiles/

http://www.dtcperspectives.com/getting-to-the-how-unlocking-identification-personalization-and-the-regulatory-landscape/