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Best Practices to Increase Brand Reputation



Best Practices to Increase Brand Reputation

Traditional, conventional display marketing is out; programmatic advertising is in. 

Certainly, the evidence from digital ad spend points that way, and it makes sense. It’s an even more effective tool that lets you accomplish your advertising goals without breaking the bank.

Programmatic advertising, after all, works by putting hyper-specific data about your target consumers to work to create hyper-targeted ad campaigns that ultimately yield a higher ROI. 

Programmatic marketing is a particularly effective tool for marketers in the B2B sector—businesses who don’t need to ask: What is a fixed VoIP phone number, for example—because the target audience is typically much smaller, and thus ideal for this data-driven approach. 

In this article, we’ll set out six programmatic advertising best practices that build brand reputation and produce results.

Let’s dive in!

What is programmatic advertising? 

Programmatic advertising is the automated buying and selling of digital advertising on a publisher’s website or app, where you eke out your digital ad space in real-time.


Automation, in general, is a process that saves businesses time and money. And while in traditional advertising, marketers typically cast a wide net and target everyone who visits a website, programmatic advertising uses data to target a specific audience rather than the location. 

When customers interact with DSPs (demand-side platforms) such as mobile apps, video, or CTVs (connected TVs), advertisers bid for impressions targeted to users based on hyper-specific data points around demographics, cookie data, and information about online behavior. 

So, brands can target subsets of individuals and, as a result, improve their messaging and advertising. Programmatic advertising thus finetunes the media buying process, all in the time it takes for a web page to load. 

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Want to actually live up to the mantra to get your message in front of the right people at the right time? 

Let’s get into six best practices for effective programmatic advertising.

  1. Plan and set goals

If you’re thinking of using programmatic ads to boost efficiency and improve your brand, start as you mean to go on. Do your homework, devise goals, and give plenty of thought to executing a creative strategy. 

Stating the obvious, sure, but worth reiterating. Clarity on your performance goals—what counts as a successful strategy and how you’ll measure your performance—will underpin your entire campaign. 

Here are a few common goals that programmatic advertising can help you with:

  • Building brand awareness
  • Reaching and targeting new audiences
  • Keeping existing customers and targeting them better
  • Driving higher ROI
  • Improving ad visibility
  • Limiting ad spend.  

Programmatic advertising allows marketers to benefit by combining the power of automation, data, and creativity to get more from their display ad performance. When we suggest getting creative—we’re talking cold email masterclass creative—we mean both in the design of the display ad and the way marketers can ingeniously target and retarget customers. 

Marketers can make ads more relevant based on several factors, such as location, device, demographics, and even the weather. And creatively adjust their ads to deliver that killer message. 


You can create campaigns that align with your goals by choosing between or combining the following display media to hit the right note:

Banner ads help raise your brand awareness and reach a broad audience. They engage customers from the top of the funnel, which keeps your brand top of mind and allows you to grab users’ attention before they’ve even become aware of your product.

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Video ads can be used in campaigns to introduce sound and movement, and enhance the stories you tell to win your audience’s attention. The benefit of using video ads is in driving awareness of your brand.

Native ads are editorial-style advertisements, carefully blended in website content to have a natural, unobtrusive feel. As such, they’re effective in generating high-quality targeted traffic for your site.

In-app ads are notable for their solid click-through rates. These ads can be a good way of driving new users to your mobile app through paid mobile user acquisition and various related marketing activities. 

2. Target better

With the ability to target your audience, you can highly optimize a programmatic media campaign and, most importantly, get more bang from your media budget. 

The key is dividing your traditional audience into specific subsets or segments according to preconceived criteria. 


Tailoring ads and displaying them to target audiences in your DSP can help you minimize wasted ad budget on the wrong audience.

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And reaching those individuals most likely to be interested in your products and services with relevant, creative, and customized content is your surest bet to turn users into converts. 

A data-driven approach to figuring out the ideal iterations of a given ad is the way to go, and machine learning tools can help allow you to get the right messaging to the right individuals. 

For example, algorithms can determine the most effective location to place the ad, be it a big platform such as Facebook or a more specialized site with a higher concentration of targeted users. 

3. Choose your DSP thoughtfully

Choose a platform that works within your ad spend. You bid to get displayed on a DSP once you’ve launched your campaign, which involves competing with other advertisers for ad inventory through an automated bidding system.

With a head-spinning array of variables to consider when choosing the right platform, some brands opt to partner with tech experts who can help you get the most of the platform features. 

Some things to look out for in each platform: 

  • Their targeting options
  • The quality of its interface 
  • Tech support when you face technical difficulties
  • The amount of inventory each makes available
  • Whether they support the top creative ad formats mentioned above and allow you to engage in different deals for inventory options. 

4. Go with a data-driven strategy

There are three types of data you can use to target the right customers with the right messages. 

First-party data is free, and arguably your most valuable data since it’s information collected directly from your audience. It includes information about their online behavior, interests, and activities on your site and apps. 

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You can also gather this data from CRMs, survey results, and customer feedback, and use it to make predictions and deliver the kind of personalized content that attracts new customers.

Second-party data is, basically, the first-party data you purchase from another source on a private marketplace.

Third-party data is information pulled from various sources by big data aggregators. While not exclusive, as competitors can also access it, it can work well in combination with your own first-party data to enrich your insights and help you reach a broader audience. 

While the pandemic spotlighted the costs of remote working for staff—worker visibility, for example—it also drove companies to look to incorporate AI into their marketing strategy to keep their expenses down.

With the imminent end of third-party cookies, that trend will surely accelerate as marketers are tasked with finding the most efficient ways to reach audiences. 

5. Join up advertising data with analytics data 


The best way to optimize your audience targeting is to connect your advertising data with your analytics. Your analytics not only inform which users you target, but they also yield invaluable insight about when to target individuals and audiences along the customer journey.

By running ads that resonate with users where and when you find them in the conversion funnel, you can create the kind of content and ads that make their mark and build your brand. 

Take B2B ad campaigns where the target audience of key decision-makers is rather specialized and tech-savvy—unlikely to have to ask: “What is cloud PBX?”, for instance. They’re extremely well suited to the nimbleness of a programmatic marketing approach. 

On top of that, connecting your data sources to deliver more relevant ads that add value for your audience also allows you to exclude users from specific programmatic campaigns and saves you from wasting media spend on misplaced ads.

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Choose a capable DSP, and programmatic marketing helps establish a single source of truth for monitoring and measuring your marketing performance. And it supports you in integrating your programmatic buying into your overall marketing efforts. 

With a constantly shifting digital landscape, you must cash in on rich real-time feedback insights that will inform your experiments. 

Working with the right partner and tools lets you develop attribution models that provide a granular understanding of what’s working across different touchpoints and what’s not. 

6. Implement frequency caps 


A simple, specific trick to getting the most out of your programmatic performance is implementing frequency caps. 

These allow you to control how many impressions specific users see per month, week, or hour. 

Integrating your data with your analytics platform lets you exclude specific users across your channels from seeing duplicate ads. That simultaneously prevents you from, again, squandering valuable programmatic media spend while turning off users with redundant ads that actually decrease the chance that they’ll convert. 

Fail to add value, and your customers can quickly grow frustrated. This can be hugely detrimental to the reputation of your brand. Particularly in contrast to competitors that purvey fresh, engaging, and relevant content every time.

Experiment and test your outcomes with frequency capping. 

Over to you

Consumers are already numb to digital ads, but programmatic advertising can deliver fresher content in more engaging ways, provided you do it well. 

In allowing for the continuous optimization of KPIs throughout the buyer journey, it can help you better allocate your ad spend and improve the unique reach of your marketing efforts. 

And when you add value, avoid annoying duplication, and make people’s lives simpler, you build your brand reputation.


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Definition and How to Calculate a Lead Score



Definition and How to Calculate a Lead Score

Generating a high volume of leads is one thing while identifying which prospects are most likely to convert into customers is another. To put it differently, it’s all about quality over quantity. 

That’s where lead scoring comes in. Let’s take a look at what lead scoring is and how you can calculate it effectively.

What is Lead Scoring? 

In short, lead scoring involves gauging your prospects’ quality to determine which leads are worth pursuing and which ones are not. Usually, this process works on a point-based system. 

You assign prospects points according to various attributes. More specifically, you can score leads based on implicit and explicit data. 

Explicit information refers to factual data that your leads have confirmed through a phone call or by completing a form. On the other hand, implicit data is based on the information you already possess, like purchase history. 

You can then break down both data sets into demographic and behavioral data. Demographic data refers to your leads’ company size, geographical information, or industry, while behavioral data focuses on information based on the actions your leads took, like form submissions. 

What are the Benefits of Lead Scoring?  

For starters, lead scoring makes the sales process more efficient. By identifying qualified and unqualified leads, you’ll no longer need to spend time cold-calling or personalizing sales emails to leads that will likely not bring any value to your business. 


By getting bad leads out of the equation, you’ll likely be able to increase conversions with fewer sales representatives. 

Consequently, lead scoring helps you save time and money. It also helps shift your sales efforts to high-value leads for better results. 

In addition, a lead scoring system helps you improve your marketing strategy. You’ll get to identify which marketing channels bring in the most valuable leads, thus allowing you to determine which channels are worth investing in and which ones are not. 

Moreover, lead scoring will improve the nurturing process, as it allows you to pinpoint where prospects are situated in the sales cycle. 

You can use that information to send content dedicated to leads during each sales cycle stage. Consequently, you can create more meaningful relationships and close deals quicker.

Lastly, evaluating leads will help you get your sales and marketing teams on the same page. As mentioned earlier, having a lead scoring system requires data. 

This information helps marketers understand what type of prospects are most likely to convert and how to create targeted campaigns to attract them. The marketing department can then hand over sales-ready leads to the sales team and help them generate more sales

How to Score Leads Manually

Lead-to-Customer Conversion Rate

The lead-to-customer conversion rate represents the percentage of the company’s qualified leads that resulted in a sale. To calculate this metric, divide the number of qualified prospects that generated conversions by the total number of qualified leads. 


Why is this metric important? It essentially acts as your sales team’s benchmark. It allows you to assess the performance of your sales funnel and helps you stack up multiple marketing channels against each other to identify which ones are most effective in generating high-quality leads.

Choose the Right Attributes for Your Model 

Attributes are the backbone of your lead scoring model. They help you define and identify the characteristics of sales-ready prospects and give you an idea of how to improve lead quality.

That’s where the data we mentioned earlier comes in. First, identify the prospects that bring the most value to your business. 

Second, take your implicit and explicit data sets and find similarities between your high-value leads by examining demographic and behavioral data. Upon reviewing the data, identify the characteristics that define high-quality prospects and assign the attributes accordingly.

Determine the Close Rate for All Attributes

Next up, it’s time to determine which attribute is more valuable than the other. You’ll first need to calculate the close rate for each attribute to do this. More specifically, identify how many prospects turn into customers depending on their behavior or demographics. 

For instance, you could calculate the close rate of people who sign-up for your newsletter, follow you on social media, or determine the close rate of prospects within various regions or niches. 

Assign Point Values

Upon determining the close rates for each attribute, you’ll need to compare them and prioritize one over another.  

For instance, recent Instagram statistics show that marketers grade influencer marketing as their fastest-expanding customer acquisition channel. So you may find out that more prospects from influencer campaigns turn into customers than prospects who signed up for your newsletter.


In that case, the former attribute is more valuable. Repeat the process for each specific attribute to define the characteristics that accurately reflect lead quality. 

Furthermore, compare the close rates of your attributes to your overall close rate. This will act as a reference point when assigning scores for your attributes. 

For example, if newsletter sign-ups have a close rate of 15%, whereas your overall close rate is one percent, you could assign each lead that registers to your newsletter 15 points. 

Setting a minimum score threshold is also recommended to draw the line between qualified and unqualified prospects easier. For instance, leads with a score below 50/100 points may not be worth pursuing. 

Other Types of Lead Scoring

Aside from the manual approach, there are also other methods of scoring leads. More specifically, predictive and logistics regression lead scoring. 

Predictive lead scoring might be your best bet if you’re looking to save time. This method uses machine learning to go through prospect data to find common points between leads that convert and leads that do not and rank each prospect based on their likelihood of converting. 

Predictive lead scoring eliminates the need to manually sift through data to identify valuable attributes and minimizes the risk of human errors. 

Moreover, since predictive lead scoring systems use machine learning technology, you won’t need to optimize your follow-up strategy manually. 


On the other hand, the main strength of logistic regression lead scoring systems lies in their accuracy, as it considers how all customer attributes interact with one another. 

This is a data mining technique that uses Microsoft Excel. It works by building a formula in the spreadsheet which will reveal the probability of turning a prospect into a customer. 


Overall, lead scoring is crucial for identifying high-value prospects and giving you an insight into how you could lower your cost per lead

Start by determining your lead-to-conversion late. After that, choose the right attributes depending on your customer information, calculate the close rate of each attribute, and sort them based on their importance.

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