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How an Effective Data Flywheel Delivers Compounding Marketing Growth

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Every time a business interacts with client’s offline or online, new data footprints are created. These data footprints can take on various embodiments. From physical forms to online drop forms, questionnaires, to website’s history and Facebook posts that have been clicked.

A massive amount of data is available for collection and mining. This data can be aggregated and analyzed to make consumer behavior predictions for retargeting purposes. But not many businesses are aware how this data can be assembled and re-pivoted to provide invaluable insights that boost bottom lines.

So how can you build a deep, dynamic, closed-loop data set, especially if you’re starting from ground zero?

Data Collection — The Backbone for a Dynamic Data Flywheel

Managing data is a team sport and datasetsDB is here to help you, in each step throughout customer journey.

We spent decades helping businesses of all sizes to scale up using data-driven consumer insights. By using a comprehensive and additive approach, we enable business and technology leaders get the most value from their data.

As we help businesses improve their data strategy, we noticed 3 common problems across industries:

  1. Most businesses collect extraordinarily little data when acquiring new customers.
  2. Most businesses do not have a short or long-term data enhancement or consumer segmentation strategy.
  3. While most businesses would like to become data-driven powerhouses, they don’t know where and how to start

Our scientific approach towards consumer segmentation has helped turn around ailing businesses. And simple efforts to append additional data attributes to existing data can exponentially kick start growth for new start-ups.

As data can be fragmented, we helped businesses append additional consumer attributes that have not collected. Putting these additional attributes together connects all the dots to give you actionable datasets that are powerful enough to build a complete profile of your client or prospect.

And herein lays the perfect opportunity for you to jump-start your data flywheel.

What Is A Data Flywheel?

A data flywheel is a concept whereby the more users you onboard, the more data you will have to give you better insights of your users. With vast information, you can build better algorithms, serve up targeted products and this ultimately leads to positive user experience.

Here’s what most companies strive to achieve by moving from funnel marketing to flywheel marketing. Businesses that successfully use the data flywheel capture data and efficiently spin the data flywheel fast. This culture must permeate throughout the organization, resulting in minimized friction as customer complaints are addressed promptly.

To put things into perspective, online retail stores have mastered the benefits of data flywheel far better than their brick-and-mortar cousins. These online retailers collect user information at every touch point, enabling them to deliver highly personalized customer experience. This leads to generating more sales and improving the overall client experience. As data collection grows and customer engagement improves, the flywheel spins faster and faster with both elements feeding off each other.

To design a seamless data flywheel, you have to rethink the relationship between your product and data. Data collection cannot be a series of one-off exercises; it must be internalized within the organization and integrated into the core product to offer a holistic client experience.

Essentially, every time a user engages with your product, you must collect data from that interaction. This systematic process lets you use the data in the future to serve up exciting promotions and new services, across all users in the network.

You must put in place a robust process to capture unique data on how your product is used. The captured data will provide insights on your product functionality and service level. This allows you to enhance your products and services to meet consumer needs. This data value chain ensures that customer motivations are aligned with business models.

And this can only be achieved when you compound the value of your proprietary data set, and leverage on big data to delight customers.

How an Effective Data Flywheel Delivers Compounding Growth

Building an effective data flywheel is the key to achieve “compounding growth”. Once your business enters this phase, every new customer you add contributes to the data set. Thus, this makes the product better. When your product offering addresses consumer gaps and hits the mark of “wants”, you will be able to attract more customers. This kick starts the cycle of making the data set tighter and more effective.

For this model to be constructive, you have to rethink how data is used across your customer base. You must harness the data from every user in your network to improve the user experience.

What Is Marketing Hacking and Why It Is Important

But no company starts with this user-driven flywheel. They start with marketing hacking.

Marketing hacking may sound like a notorious narrative but it describes the act of pairing your business’s existing data with other data fields. The other data fields such as popular email domain provide, zip codes, geo-demographic data, and gender will enrich your customer profile and allow you to target market with clarity. This pull strategy demonstrates that you understand your clients better and this will help you tweak your offering to meet today’s ever-changing customer needs.

Marketing hacking can be placed along a spectrum, ranging from aggregation hacks to creation hacks. The former starts with existing data sets pooled together cohesively. This is a relatively straightforward method that is widely used by startups and new businesses.

i) Aggregation Hacks

Aggregation hacks can include pairing zip code information with rich geo-demographic data fields. With aggregation hacks, you can effectively pinpoint segments of customers with strong purchasing power and use age group dimensions to gauge buying propensity. Above all, you can devise propensity models that project product needs basing on employment and occupation status.

ii) Creation Hacks

On the other end of the spectrum are creation hacks. Just as its name suggests, creation hacks involve the generation of data that does not exist before (at least in a structured manner). These tend to be harder and are thus a rarer starting point.

Another dimension that influences the outcome of these hacks is how proprietary is the merged data. While the ultimate goal is to achieve a defensible, compounding data set, using less proprietary data can be effective if you move quickly.

The hacks you embark on are influenced by the type of assets you have to start with. To exemplify, established companies may have unstructured data sets to work with.

On the other hand, startups with fewer assets by definition often have to be more creative. Indeed, the best founders I worked with are extremely creative when it comes to designing data hacks.

As we enter the era of Great Commoditization, CEOs of all business ranging from freshly minted startups to established incumbents will need to answer two critical new questions: Which marketing dataset hack or hacks are you pursuing, and how will they lead you to a flywheel?

Here’s how datasetsDB can Help Accelerate Your Data Flywheel

datasetsDB offers several datasets that could be great starting points to get your data flywheel turning in the right direction. With datasetsDB’s help, you can realign your entire company growth strategy around the data flywheel. Using the flywheel model, you can attract new clients, engage, and delight them. By putting your customer’s first, you provide an amazing experience throughout the customer journey.

Here’s how datasetsDB’s expertise allows you to get the greatest value out of your existing data. There are 3 unique datasets you can start with:

1. List of Popular Email Domain Providers

This list of Popular Email Domain Providers is the largest dataset of 12,000 free email domain providers ranging from the most popular (such as Gmail, Yahoo, AOL) to lesser-known names (such as optonline.net, hallco.org, windstream.net, etc.)

With this list of email domain providers, you can crosscheck email domains of existing customers against our dataset and distinguish individual customers from business email address users.

Research shows that business email address users are 25% more engaged than popular email domain users. Moreover, the average order value from business email account users is 146% higher than someone using individual domain providers.

By flagging out customers as “personal” vs. “business” based on the email address, you can prioritize targeting business email users to increase your average order value, product engagement and generate higher conversion rates.

2. List of 48k Zip Codes with 283 Attributes

With more than 48,349 US Zip Codes enriched with geo-demographics data fields, this is the most comprehensive dataset of US zip codes ideal for location-based segmentation and targeting.

We boasts 283 fields of geo-demographics data attributes ranging from city, state, county, population density to household Income, age group, marital status, employment, educational attainment, commuting time, and more.

Above all, these zip codes are refreshed regularly to provide unparalleled accuracy for geo-location marketing. Besides, our data sets provide marketers all the essential information to ascertain a zip code’s business potential.

3. List of First Names by Gender Dataset

You will get instant access to 177k First Names by Gender (Female / Male / Unisex)

With this list of gender dataset, you can start personalizing your marketing messages and increase your conversion rates by 35%, email open rates by 50%, click-through rates by 25% and sales by over 25%

With 135 years’ worth of data covering 365 million people, our database is credible and easy-to-use. You will enjoy unrivaled access to the most reputable database of names by gender, serving you with exceptional data coverage and precise targeting.

customer segmentation customers by gender data flywheel datasets for marketing datasets hacking geo-demographics marketing location based marketing marketing hacking marketing segmenation

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