These days, we have more data on our customers and current market trends than ever before. However, many companies struggle when turning data into insights.

To stay competitive, you need to know what data to collect, how to gather it and how to apply the information effectively to make intelligent decisions.

And that’s what this article is all about. Let’s look at how you can use data to optimize your sales and marketing strategies, get an edge on the competition and, ultimately, drive more revenue.

The Benefits of Data Analytics in Sales and Marketing

In essence, data analytics lets you take all the data gathered during your sales and marketing processes and present it in a way that’s easily understood and actionable for you and your team.

The type of data collected ranges from customer demographics and behavior to sales teams’ metrics. Once collected, you can use this data to streamline business processes, make accurate predictions, set sales goals and act on emerging opportunities.

When you take full advantage of data analysis, you can expect the following benefits:

1. Higher Revenue and Lower Costs

The most direct and relevant benefit is the revenue growth potential. Research by BARC found that companies that use data saw their revenue increase by an average of 8% while simultaneously lowering costs by 10%.

Ultimately, that means greater profit margins for the business.

2. Keep Your Customers

Holding on to current customers is equally important as finding new ones. In fact, improving your customer retention rate by 5% can boost profits by a whopping 95%. Data analysis can help pinpoint when and where customers fall off. Maybe it’s too many emails that feel like spam or the content doesn’t match what they’re looking for.

3. Understand Customer Behavior with Data Analytics

Another major benefit of data analytics in sales is that it helps you hone in on your customer’s behavior and patterns.

For example, if you sell paper clips in bulk, you can use data analysis to determine when customers will likely run out and need to restock. You’ll know when it’s time to increase production and when it’s time to ease up so you don’t have inventory collecting dust.

4. Improved Segmentation

Data analysis also helps you fine-tune your lead segmentation so you can effectively go after high-value targets and manage any choke points down your sales pipeline. It also helps with customer segmentation so you can optimize your value proposition to better give your clientele what they want.

For example, you can design a marketing campaign that emails clients who purchased paper clips within the past six months but didn’t buy any staples. Businesses that use paper clips likely need staples as well (hello, cross-selling), so you can offer a 10% discount for customers who buy both this time around.

5. More Accurate Forecasts

Forecasts are where data analytics truly shines. Instead of taking shots in the dark, you can make data-driven decisions based on historical data you’ve collected. It helps you identify sales opportunities and predict potential pitfalls. Plus, you’ll get the tools to capitalize on them.

You can use data analytics to determine how marketing costs impact sales, which in turn will help you figure out your marketing ROI. But why stop there? Analyze MQL channels by how profitable the leads become and double down on the channels that drive profits in the long term.

How to Practically Apply Data Analytics in Sales and Marketing

The key to getting the most out of data analytics is knowing what data is valid and what’s not. Of course, that depends on what you want to analyze. Let’s take a look at some of the most common metrics and how to apply data analytics to get the right results:

1. Revenue Growth

Revenue growth simply looks at the revenue generated over a period of time to determine if it’s going up or down. It can be monthly, quarterly or annually, and it can help you spot trends like whether you need to increase production, downsize, etc.

The most critical data point here is, well, revenue. For example, let’s say you want to see your year-over-year revenue growth. You made $100,000 last year and $150,000 this year.

You use the formula:

((Current Revenue - Previous Revenue) / Previous Revenue) x 100.

So that would be:

((150,000 - 100,000) / 100,000) x 100 = 50%

That means your revenue grew by 50%. If the number is positive, your revenue is increasing. If it’s negative, your revenue is decreasing. You’ll need to make some adjustments.

2. Customer Segmentation

You can segment your customers in several ways, including demographics, behavior, geography, technographics and more. Demographics is by far the most commonly used.

The type of data here includes:

  • Age
  • Gender
  • Location
  • Occupation
  • Income
  • Marital Status

For example, you can create one targeted campaign for customers aged 18-24 and then have another group aged 25-34. When you have your groups segmented, you can determine what content works best for them or which channel is the right choice.

You can get this type of data in many ways, but a few of the most common methods include surveys, focus groups and direct customer interviews.

3. Customer Journey Analysis

Customer journey lets you gauge the customer’s experience through your sales pipeline and measure the performance of your marketing efforts.

When the customer journey is influenced correctly, it means more conversion and sales. On the other hand, a poor customer journey can cause you to lose existing or potential customers.

Since there are various touch points where the customer engages with your company, there are several key data points you need to track, including:

  • Website visits
  • Conversion rate
  • Cost per conversion
  • Sales
  • New customer revenue
  • Customer lifetime value (CLV)
  • Net promoter score (NPS)
  • Return rate
  • User engagement
  • Impressions
  • Churn rate
  • Retention rate

Of course, this list isn’t exhaustive. For example, you can track metrics such as in-app purchases if you’re a SaaS company with an app.

The specific metrics depend on your business, but the goal is to measure each point along the customer’s journey – from start to finish.

4. Predictive Lead Scoring Analytics

Predictive lead scoring uses customer activities to help you prioritize leads and go after the targets most likely to end with a won deal. The data you need to get the most out of predictive lead scoring comes from two sources: online and offline.

Online sources include data points such as:

Whereas offline data points include:

  • CRM data
  • Demographics
  • Purchase history
  • Live event attendance
  • Company size
  • Financial details (e.g., the customer had a previous subscription)

Of course, the specific data points will vary depending on your business and industry. However, if you want accurate and actionable lead scoring, you need to use both offline and online data sources.

5. Sales Forecasting and Projections

Sales forecasting helps you accurately predict the number of sales your company will make over a specific period – be it weekly, monthly, quarterly or annually. Sales projections are vital, but they require data from different sources, including the following:

  • Historical data: This is the company's past sales data to help you identify patterns and trends. For example, if you’ve seen sales spikes during the summer for the past two years, you can expect the seasonality to affect your numbers again.
  • Market data: Market research looks at industry trends, demographics, customer buying habits, current macro-events (e.g., new regulations) and seasonality to predict future sales. You can also use this data analysis to identify new opportunities and risks.
  • Competitor data: While this could fall under market data, specifically looking at your competitors can help with sales forecasting and strategy. This includes looking at their prices, customers and overall performance, so you know what’s working for them and what’s not – and how that might affect your business (decisions).

6. Optimizing Marketing Campaigns

Data analytics is vital for understanding how well your marketing campaigns are doing and what you need to do to optimize your ROI. Some of the key data points you can use to help gauge your campaign’s performance include (but are not limited to):

  • Website visits
  • Click-through rates (CTR)
  • Return on ad spend
  • Conversion rate
  • Customer acquisition cost (CAC)
  • Customer lifetime value (LTV)

Again, the key is to pick KPIs that match your unique marketing goals – be it increasing brand awareness or driving more sales.

Suppose your marketing team helps your sales team generate leads. In that case, you’ll want to look at the information that specifically testifies to the quality of lead generation channels and subsequent lifetime values.

7. Optimizing Pricing Strategies

Want to stay competitive? Reconsider your pricing strategy. The price of your product/service depends on more than how much you want to make for it. You need to consider the following data points:

  • Competitor pricing
  • Price elasticity
  • Customer preferences
  • Customer demographics
  • Customer behavior
  • Gross profit
  • Revenue
  • Quantity
  • Sales data

When you effectively analyze your data, you can use it to ensure you and the customer are getting the most value. You can also use this data to inform how you manage promotions and discounts. For example, use customer demographics and preferences to create targeted promotions for price-sensitive segments.

8. Churn Prediction

Churn prediction takes your churn rate (or churn attrition) and combines it to give customers’ a score for the likelihood they will jump ship.

First, you will need your churn rate, which you can find with this formula:

(Customers Lost  / Total Customers at Start of Time Period) x 100

Once you have that, you will need other data points, such as:

  • Customer demographics
  • Number of products/services purchases
  • Product/service usage
  • Customer support tickets/emails
  • Time to resolution
  • Customer satisfaction

Keep in mind that these models take time to identify commonalities between which customers stay or churn. However, they’re worth the wait because you’ll be able to group customers based on their likelihood of churning, so your team can proactively intervene.

9. Customer Retention and Lifetime Value Optimization

Customer lifetime value (LTV) refers to the total value a customer brings to your business over the entire course of the relationship. It includes not only what they buy but also their referrals.

Of course, keeping the customer and reducing churn are two of the most critical aspects of LTV. So, with LTV and customer retention optimization, you can pinpoint those high-value targets, inform your marketing strategy and improve the overall customer experience.

If you’re looking to retain as many customers as possible, start collecting the following data:

  • Customer demographics
  • CAC
  • Average order value
  • Churn rate
  • Website analytics (visits, page scroll, etc.)
  • Social media interactions
  • Product usage

10. Sales Performance Analysis

Sales performance analysis gives you data-backed insights on the performance of your sales team, either as a whole or for the individual rep. If you’re a manager, you can use this data to set goals, forecast sales, improve the sales funnel and increase revenue.

Of course, the specifics depend on your goals, but here’s a good starting point for which data points you need to track:

  • Number of sales
  • Revenue generated
  • Profit generated
  • Average deal size
  • Deals won/lost
  • Lead response time
  • Conversion rate
  • Emails sent
  • Meetings scheduled
  • Churn rate
  • Retention rate
  • Average sales cycle length

Analyzing sales performance helps you identify your top performers. It also helps you find areas for improvement and coaching opportunities. When you understand how your team is doing, you can figure out what you need to do to make them even better at their jobs.

The Key Challenges in Data Analytics in Sales and Marketing (and How to Overcome Them)

Of course, nothing is quite as simple. There are a few common challenges that typically prevent businesses from getting the full benefits of data analytics.

Data Quality and Integration

One primary issue is ensuring data quality and integration, especially when dealing with diverse sources. Silos between sales and marketing departments disrupt the flow of information, leading to fragmented and inaccurate insights.

The best solution is to invest in robust data management systems to maintain data accuracy, consistency and accessibility across all departments.

Another point is to foster collaboration between sales and marketing teams – focusing on shared goals and implementing integrated platforms to help give a comprehensive view of customer interactions.

Skill Gap and Training

There’s no doubt data analytics requires someone skilled to deal with complex datasets and come up with valuable insights. That’s where the skill gap rears its ugly head.

The best way to overcome this hurdle is proactively investing in training programs to give your current team the skills they need. Alternatively, you seek out individuals with the necessary skill set. Still, make sure your team is fully trained on the best practices.

Complexity of Analytics Tools and Tech Infrastructure Constraints

The complexity of analytics tools and limitations in tech infrastructure can hinder your data analytics strategies. Make sure you’re using tools that aren’t overly complicated or providing your team with training and resources to get the most out of them.

There are several ways to address infrastructure constraints, such as upgrading your systems, investing in cloud-based solutions and ensuring your system can handle the amount of data your business collects.

Defining Clear Metrics and Objectives

Without well-defined metrics and objectives, you will struggle to gain meaningful insights from your data. Make sure the KPIs you track match your business goals and consistently re-assess the metrics you track to ensure they’re still relevant.

Interpreting and Applying Insights

Interpreting data is part science and part art. It can be challenging to translate raw data into actionable insight. One way to handle this issue is to encourage a data-driven culture that values not only data collection but also the interpretation of insights.

This means including collaborative workshops and making use of data visualization tools to help you get a deeper understanding of the data.

Unlocking Your Business’ Full Potential with Data Analytics

In the age of big data, it’s imperative you understand what to collect and how you can use it to drive decisions for sales and marketing. However, it’s not enough to hire a data analyst. Your entire team has to be on board.

And when your sales team is at its best, your company is even better. Give your team every edge you can with the power of data analytics.