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Market intelligence is all about valuable data that is readily available to businesses. That data helps evaluate your market position, understand your audience, identify risks and growth opportunities, understand trends and dynamics, build effective strategies, and eventually drive organization growth.
But what if the data used to glean market intelligence is bad or dirty?
Bad data leads to wrong decisions, disgruntled customers, high costs, wrong targeting, etc. Even technologies such as artificial intelligence (AI) and machine-learning (ML) require accurate data to function properly.
So much depends on data quality, and even more so if it concerns market intelligence. Data-driven organizations need to trust their data, but the scenario looks grim: some 55% of business leaders don’t trust their data assets.
Accurate data needs intelligent gathering, filtering, cleansing, validating, enriching, and formatting to make it useful and to draw meaningful market intelligence.
Attributes of Data Used to Derive Market Intelligence
Because structured and unstructured data is captured from multiple sources, such as websites, social media, and surveys, and in numerous formats, ensuring the accuracy, relevance, and usability of data remains a challenge.
Let’s begin with what constitutes high-quality data:
- Accurate: Data with no errors or outdated information, redundancies, or typos
- Complete: Data with no missing fields, values, or incomplete information.
- Relevant: Data that’s helpful for your set goals
- Valid: Data that’s verified and validated and therefore trustworthy
- Consistent: Data that remains consistent and aligns with your format
- Real-time: Data that’s updated consistently and regularly
Why Data Quality Is Important for Market Intelligence
High-quality data is a prerequisite for effective market intelligence. The following are six major reasons for that.
1. Better Audience Targeting
Bad data leads to wasted time, energy, and effort, because you never connect to the target audience. High-quality data gives direction, allowing you to focus your resources on t high-potential targets.
Broad targeting based on assumptions is wasted effort; you’re chasing customers with no interest in your services or products. With superior-quality data, targeting becomes simpler and more accurate, and marketing efforts become more focused and effective.
Your company knows who your audience is, and you even find potential targets with similar attributes. Defining your target based on demographics, preferences, and behavior patterns is a crucial starting point for your marketing efforts.
2. Building Effective Campaigns
Once you know whom to target, you need to plan your campaigns for meaningful targeting. High-quality information about your audience allows you to create relevant campaigns and develop powerful content that appeals to your target. Knowing your target’s preferences, age group, income, and any other relevant information will help you build effective campaigns that evoke the desired response from your audience.
3. Personalized Messages
Access to high-quality data on personal information such as demographics, preferences, etc. helps you send targeted personalized messages to your target audience.
People expect a personalized experience, and they get dissatisfied in the absence of personalized messaging. A full 80% of consumers are more likely to do business with a company if they get personalized experiences, and 90% said they find personalization appealing, according to a Epsilon survey.
High-quality data and personal information improves communication, allowing you to send tailored and relevant content to your target audience.
4. Building Better Relationships With Your Customers
Sending personalized messages helps, but if you get access to more personal and accurate data on your target audience, you can move a step further and anticipate their requirements, as well as plan products, services, and unique content especially for them. That helps build strong and better relations with your target, eventually helping your business growth.
However, this is a tricky situation, and data management is a necessity. Data should not have any duplicates or missing fields. For instance, if the same person gets your message twice, that will leave him or her disgruntled, and it might ruin your relationship.
5. Competitive Advantage
The cost of bad data is around 15-20% of the annual revenue of companies. That is a considerable loss, and it affects the credibility of the organization. Ensuring your data is high-quality gives you a competitive advantage against your competitors.
Moreover, gathering intelligent information on your competitors gives insight into what they are offering or not offering. You can spot the differences and accordingly plan your long-term business strategies. You can anticipate things and discover opportunities before your competitors do, take quick action, and get ahead in the race.
6. Creating Trustworthy Insights
High-quality data is critical to facilitating AI and ML. Most data-intensive automated projects fail as a consequence of poor data quality. ML is becoming popular and much needed by organizations to draw meaningful insights.
Analytical advancements, AI, and ML are advancing fast, but the technologies can be of no use with substandard data quality. Extensive, accurate, and complete data is the key to correct results. A computer algorithm working on bad data can result in deeply flawed results.
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The information age is bombarding us with volumes of data. Efficient use of that data is possible only if it is managed well and used effectively to gather market intelligence.
Most important of all is data quality. Having access to high-quality data is like finding a gold mine: You target accurately, make informed business decisions, create effective campaigns, build better relations with your customers, stay ahead of the competition, and eventually make big profits.
More Resources on Data Quality
How Bad Data Hurts B2B Companies [Infographic]
The Key Ingredient of Marketing Campaign Success: Quality Data
A Five-Point Plan for First-Party Data
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