Small businesses might not have the same personnel resources as larger ones, but they still have access to the great equalizer: data. That data creates challenges in collecting and ensuring its integrity. In short, data-driven insights come with a variety of unknowns. To really see the impact of data, small businesses need to be deliberate about how they gather, interpret and use these insights. This can provide a direct path to the insights needed and the outcomes desired for success.
We can learn a lot from data. It can provide consumer behavior insights that help inform marketing, messaging and customer experience decisions, which all combine to build awareness, engagement and sales. But data often comes with roadblocks that make it difficult to capitalize on its full potential.
Many organizations, for example, are stuck with siloed data that is scattered across disparate systems, making it nearly impossible for marketers to get a complete understanding of their customers. As a marketer, consider what would happen if you were to use only transactional data to make your decisions. You’d simply be making assumptions about how and what customers purchased and nothing about the overall preferences, interests, and dislikes that tailor customer interactions.
Integrating data from various sources ensures that a customer profile is as complete as possible. It can alert you to future discontent and identify potential customer service issues. With the right triggers in place, data integration delivers the insights small businesses need to reach out to customers should they abandon their shopping carts before making a purchase.
Even when organizations manage to overcome struggles with data silos, there are still many challenges in data collection. Accuracy can be one problem. When information is entered incorrectly – in the wrong data fields or without research – data can skew analytics and marketing results can suffer. In fact, 37% of marketers say poor data quality contributes to wasted marketing spend. Another 35% say it leads to inaccurate targeting, and 30% blame data issues for lost customers.
Outdated or incomplete information compounds the challenges of data collection. If your business doesn’t update its data regularly, simple contact data like email or mailing addresses can quickly become obsolete and prevent you from contacting your customers or prospects.
There’s also the issue of collecting too much data. Too much data can be overwhelming, and a plan is necessary to identify those data points that correlate with your KPIs and objectives. Otherwise, this wealth of information will muddle even the best data-driven business strategy. If you capture too much data or the wrong data, you lose the ability to interpret the information that’s right in front of you.
The goal is to limit your data sources to keep customer data information accurate, complete and up to date. Pulling data from every available source just makes the information even more difficult to interpret. You should always refer to your objectives to ensure you’re capturing what you need from where you need it to derive business-driven insights from your data.
Most businesses, large and small, understand the value of data. It’s the only real means for effective audience segmentation and targeting. The challenge, in many cases, is in the approach. Many think the best way to drive revenue is to hammer target audiences with emails. Unfortunately, even the most loyal customers will either tune out or unsubscribe from the barrage of emails.
Faherty, a family-run clothing brand, has struck a good balance with audience segmentation and targeting in its messaging. Instead of bombarding customers with multiple emails, it opts for a more personalized approach, tying messaging back to past engagement history or topics that resonate with its customer base – sustainable clothing, for example.
OkCupid takes a slightly different approach. The online dating platform uses audience segmentation and targeting to inform its messaging, but it also includes proprietary data. A survey by the dating site found that 60% of singles “could date someone who doesn’t speak their language very well.” Considering that people tend to share statistics more often than other types of content, this type of messaging has the potential to reach more than just OkCupid’s customer base.
Using data to segment and target audiences with the right message at the right time generally provides a better return on marketing spend. It becomes much easier to identify patterns and make correlations, helping small businesses create more actionable data insights.
Data should drive business decisions. It provides insights into target audiences and offers a great way to identify new leads. Small businesses tend to stumble when determining where exactly to start and how to use the data available to implement a data-driven strategy.
There is no exact science to guarantee that the information you’ve gathered translates to effective business strategies. But by adopting a few tried-and-true tactics, your business practices can become a reflection of your unique data insights.
You need a central means of managing data to make informed decisions about everything from product demand to messaging. But unifying data can be just as much of a challenge as its collection, largely due to the data silos that exist in most businesses.
Before unifying data, you must identify and understand your target audience. Make sure your data is accurate and complete by cross-referencing any data stored in separate silos. From there, clarify your KPIs and determine how the data will help deliver the results you’re seeking to ensure the best business-driven insights from your data.
Data is most effective when it’s focused on a desired outcome. Point yours at a set target to deliver the best results and action steps.
Segmentation will be driven by your target audience and is often based on type, behavior, engagement, journey and purchasing history. This allows you to focus on marketing efforts that target a specific audience – those who have recently purchased or expressed interest in an item similar to a new product you’re about to launch.
Travel firm Backroads uses data from past trips to put together and market personal packages for customers, a strategy that has created a new revenue stream for the company. Prospects are a different story. Segmentation becomes more about the content they’ve responded to and whether their likes, behaviors, or characteristics are similar to those of your existing customer base.
Let data aid these segments and speak to what resonates most with them. This helps teams develop audience benchmarks that build toward larger marketing goals.
So much time, money, and effort are invested in sales and marketing that working from old data becomes an exercise in futility. Update your data quarterly at the very least, especially if your business is in the B2B space. Why? Data goes stale quickly.
Even if only 5% to 10% of your data is unusable, it represents lost opportunities and wasted money. One of the easiest ways to calculate the potential cost of bad or stale data is to use the average annual employee turnover rate of 22%. If you have 1 million contact records in your CRM at a cost of $1 per record (today’s going rate), you could easily have 220,000 unusable contact records by the end of the year – a “cost” of $220,000.
Refreshed data, undoubtedly, is the most useful data. Stale insights can detract from the information that truly matters, so develop a process to continue highlighting, updating and prioritizing the information that is essential to your objective.
Marketing campaigns need to be driven by the quality of the data. If you don’t understand the audience you’re trying to reach, then your messaging will fail to resonate, and your offers won’t spark interest from customers – even if it’s a free trial.
Original Source: business.com
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