Identifying Data Traps through Big Data Development Strategy

Big data consists of extremely larger sets of internets users’ digital footprints, information and data generated by their online activities. Analyzing this data to identify step by step patterns of human virtual behavior is known as making better sense of this data.

A hand’s on resource that helps marketing and operations professional tap into the power of Big Data in order to boost their organization’s ability to seize B2C and B2B marketing Opportunities. To implement dynamic customer strategy, you must:

  • Recognize the data traps that can lie within Big Data.
  • Develop a data Strategy.

DCS requires data, if you want to test theory of how something works. For example: Newton may have figured out the gravity after the fall of first apple. More data means more data, not more knowledge. For example: Washing Machine manufacturers in India. Always remember that Data is not knowledge and Information is not wisdom.

Data Traps

Companies divide their customers into groups based on values.  Depending upon CRM, values might be gross sales, an index based on profitability estimates. An illusion of data knowing because you have good idea – a lot of data.

The problem is:

  1. Data provide a limited picture; rest is filled with assumptions.
  2. It’s like: All customers use our product in same way and love us.

Companies falls prey to data trap, makes a decision that seem appropriate but it’s not. Big data and Data warehousing technologies make transactional data easier to access and analyze. It adds layers of information that was unavailable and gives more info to marketing professionals

Trap is that; there’s so much data about one that it creates illusion of knowing rather than actual wisdom.

Fault: All customers are alike if they purchase same amount.

RFM Value Scoring

Some Companies uses:

  • Recency: How recently they purchased?
  • Frequency: How often they Purchase?
  • Monetary Scoring: How big is their average purchase?

Like washing machine manufacturers assumed that all machines were intended for Laundromats. Want one to one marketing but want to talk to customers directly. Each customer is different, so have to fill the gaps.

Transactional Data: CRM Professional falls in it. CRM Professionals treat all members of RCM decile the same.

Demographic Data: Traditional Marketers falls in it. They tend to treat all members of demographic group as same.

Identifying Data Traps through Big Data Development Strategy
Transactional Vs Demographic Data

Victims of traps are not practicing relationship marketing. They use available knowledge to create an illusion of knowing. Makes them feel like they know the market intact actions are based on incomplete picture.

Use of transactional and demographic data:

  • Help to identify event-based opportunities for dialogue or sales.
  • Aid in determining the value of a particular customer
  • Provide insights for other activities
  • Offers can be couched in right language
  • Other decisions are supported.
  • Only a part of total picture.

How to avoid a trap?

Answer is TO DEVELOP A DATA STRATEGY

Managing data is not for the faint of heart,” says John Akred, chief technology officer at data science consulting company Silicon Valley Data Science (SVDS). Acquire: Find the right data based on the decision to be made.

Data Strategy Steps

  1. Acquire: Find the right data based on the decision to be made.
  2. Analyze: Develop the right model to inform the decision maker.
  3. Apply: Use the model in making the decision, implementing or executing a strategy.
  4. Assess: Review the results to determine if the data and model were worthwhile.

Psycho-demographic Data helps us to understand lifestyles, age, gender effects. Behavioral Data such as web browsing activity. Motivational Data is specific to the product and use situation but driven by psycho-demographic helps us understand how our buyers consume our product. Transactional Data such as what was purchased and in what sequence. In addition to transactional and demographic data two key areas of data are also needed. Additional Types of data includes Motivational data and Lifestyle data.

These types of data are intertwined at the acquisition, the analysis and the application stage include;

  • “Nice to know” Category
  • Making an offer individualized to a customer.
  • Determining customer value
  • Rather than lifetime customer value thinks of ‘defined customer value’.
  • Volume of data gives you the illusion of knowing.
  • No major changes in market alters the relative value.
  • Buying only from one company, Ignores the share of wallet
  • Total purchase in that category of buyer’s budget tells about potential customer value.

Calculating share of wallet

“When buyer considers your product and against which competitive product helps to calculate share of wallet”

  • Another aspect is ‘Time’
  • Motivational data is knowledge that identifies, what drives a buyer to make a purchase.
  • Wallet size
  • Motivational knowledge is important for both B2B and B2C
  • Buyers have both personal as well as organizational needs.
  • There are few challenges:
  • Determining the size of the wallet
  • Determining the life
  • Data trap requires a lot of data to exist.
  • Facing an opposite challenge.

How you go about getting data and storing it will be different as function of scale and size. Transactional data is already present motivational and lifestyle data are not.

Data Research

Three Ways to get data

  • Observed behavior
  • Social media response
  • Front line customer employees

It determines how valuable your buyers are, their Progressive profiling, Relative value of fun shopper versus hardcore discounters, their Impact on value and acquisition strategies changes. Mattress Mack a legend in Houston known for his civic-minded and believes in sharing the wealth of his success. He realized that furniture retailing is changing just like all consumer purchasing. Mack fought with Showrooming by “Get it Today” delivery, with;

  • Aggressive pricing
  • Clever inventory management
  • Sourcing

This made showrooming next to impossible and increase in Mack’s sales and profits. Mack challenged long held assumption in retailing. According to Mack:

A furniture consumer today is likely to change over one full room, and possible more, in next 18 months. Some type of buyers will change over their public areas every 3 years. Find those buyers, capture their heart and you have first shot at all of their business. If first 2 conclusions from transactional data are drawn and stopped, it would arise into data trap. Mack began with newsletters containing stories appealing certain segments.

The Rest of Data Strategy

Data refoundation of a Dynamic Customer Strategy. Good data is required to understand:

  • How variables relate in driving customer behavior
  • Having sound data strategy

Acquisition of data can include using transactional data sets from financial data. It can also include asking customers by giving them offer and seeing responds or by asking questions over time.

Summary

Creating data strategy involves more than simply identifying what data you want or cataloging the data you have. Most common thinking is that a lot of data equates to knowledge. Many decision makers fall prey to data traps. A good data strategy includes acquiring data, analyzing data, applying knowledge and accessing the data.

Author

  • An MBA graduate specialized in Marketing, with proven abilities in digital Marketing, New product development, and advertisement. A professional Digital Marketer, blogger, web marketing services provider, Advertiser, Promotions, and Relationship Marketer. Highly motivated with a great degree of flexibility to adapt to changes, resourcefulness, and commitment to work; ambitious and capable of resolving multiple and complex issues.

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