Database Segmentation – Everything Marketers Need to Know      

Database Segmentation – Everything Marketers Need to Know

Database Segmentation – Everything Marketers Need to Know

By Clara Lincy | Blog | October 20, 2018

Segmentation is a classification of the database depending on the benchmark. Marketers use the demography information to bifurcate the profitable customers into manageable groups. It helps digital marketing experts to roll out targeted campaigns without significant overhead. Marketers flexibly use segmentation strategy to get optimum results. While some prefer more straightforward database segmentation such as gender type, others may have an elaborate segmentation pattern that accounts search data to classify users based on interest groups.

Why Should You Segment?

Segmentation can reduce the distance between marketers and goals. In any marketing campaign, the fastest way to reach the right customer with relevant content is regarded as an optimal strategy. When the recipients receive content that offers satisfaction, it increases the likelihood of conversion. While in contrast, an irrelevant content can irk the recipients and may negatively impact the prospecting effort. Relevancy is a fine line that separates obliteration of reputation and meritorious closure of sales.

With the Internet facilitating greater penetration, it has opened fresh avenues for an enriched user experience that a marketer can offer clients. As customer expectation continues to grow, marketers are leaving no stone unturned. They are continuously enriching the user experience online and other multi-channel platforms to offer an immersive experience. However, marketers still fumble around when consumers demand an exclusive experience. That is why list segmentation becomes more significant for businesses to address such challenges.

Going further, segmentation helps you deliver tailored contents to the right recipient because it is a process based on the demography behavior.

Types of Segmentation

There is no fixed approach to database segmentation. It depends on the marketer’s preference as to what exactly he or she intends to elicit from the consumers. However, there are four common data types that customers consider valuable.

  • Demographics:Demographics refer to the characteristics that distinctively describe an individual such as age and gender.
  • Lifestyle: The characteristic that influences data segmentation of an individual contact based on his/her lifestyle includes geographic location and wealth information.
  • Behavior:Data sets that identify users based on their search for products, services, and frequency of interaction at online touch points.
  • Value: Evaluation of customer worthiness based on his or her transaction history. Older data serves as a reference to identify profitable customers.

Drawbacks of Segmentation

    • Dirty Data

The worthiness of your database is ultimately responsible for churning favorable results. A quality database is up to date and contains verifiable contacts of segmented users serving as a reliable roadmap to marketers.

It is essential to upkeep all the existing database for extracting maximum results. Data must be periodically cleaned to remove old contacts. It helps you avoid sending different campaigns to the same receiver.

    • Definition of Dataset

All data sets must be relatable and consistent with the segmentation. Set a description of a qualified customer. Define what qualities make them more favorable to your business. An incomplete definition can yield subpar results.

    • Handpicking the Best-fit Customer

Database segmentation is a process of developing a piece of knowledge that describes a qualified customer so that they can be managed collectively without loss of time and resources. Hence, segmentation should never be the sole basis to handpick only desirable customer.

Segmentation allows dilating returns from each segmented groups without the need for regular supervision into the nitty-gritty.

Verifying the Segmentation

Segmentation can serve a broad range of purpose. If you intend to use it for identification of potential or profitable consumers, then your database segmentation process must have the following qualities.

1. Identical Attributes

The purpose of segmentation is to help marketers send targeted messages and campaign to people of equal interest.

2. Distinguishing One Segment From Another

To make the targeted campaign a success, it is important to have a clear distinction between the two segments.

3. Usable Quantity

Ample collection of the segmented database is necessary to make any efforts purposeful. With handful data, it is impossible to derive profitable returns.

4. Accuracy and Reachability

The vague definition of segments can mean less efficiency while targeting individuals. However, if one can describe segments precisely with relatable terms that outline the substance within each category, it can appear more meaningful.

5. Applicability

If you do not have a product or service that can be useful to the segmented audience, then database segmentation will make little or no difference to your marketing efforts.

Five Steps to Implementing Segmentation

Here are the different segmentation available and how it plays a significant role in your business. You can implement segmentation strategy in the following manner:

1. You can set definition for goals so that the segmentation is clearly defined in the initial stages. It helps to identify opportunities across a channel and enhances the conversion rate. Moreover, if you have multiple priorities, then segment them accordingly so that the efforts don’t stray from the purpose.

2. Build customer profile by acquiring data from every available source. Funnel every data into the segmentation framework to create an accurate representation of a profitable customer.

3. Review the data that you have acquired to handpick variables that are most desirable for database segmentation. Suppose, if you are aiming to connect with users of particular technology, you can analyze their transaction data and the duration an individual spends searching for relevant products or services. Such information may be useful for a useful digital marketing strategy.

4. Run tests to determine the data quality. Repeated testing of data will reinforce confidence in their real-time usage. A reliable data will entice improved segmentation.

5. Since database segmentation isn’t a one-time affair, results from all testing can be used for future optimization. Therefore, data from results can help solve multiple concerns such as organizational alignment with goals, market focus, and accuracy of the database.

The Future of Segmentation

In the future, simple segmentation to identify users based on industry and income groups aren’t going to resolve a full purpose.

As markets continue to expand viciously, it is going to leave a vast space for new competition, which means newer benchmarks to identify niche groups will take the spotlight.

As we pace into future, marketers will deal with newer, unique data unlike the ones used in simple database segmentation. This will help them curate data based on forecasts in the marketing realm.

For instance, you may send a campaign to a technology prospect interested in a specific IBM product on Sunday. However, you may layer the campaign with an offer to help them track the campaign in real-time which otherwise might have cost extra. Meanwhile, you can target another prospect on Monday with an entirely different proposal.
A tailored campaign can do wonders for marketing. It can make recipients see an immediate value and entice them to become your subscriber. Hence database segmentation helps to hit high conversion rate and higher ROI. Thus, segmenting your data can help bring more effectiveness into your marketing campaigns.

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