Category: Audiences & Segmentation

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Audiences & Segmentation

What Is Lookalike Modeling? All You Need To Know in 2026

lookalike modeling

One significant challenge marketers face is how they can grow their audiences once they want to achieve scale.

Growing targets will always mean that marketers need to reach more people. The problem that marketers encounter is how to grow these audiences while keeping them relevant to their product or proposition.

Expanding your audience beyond your current database is crucial to achieving future growth. What digital tools for marketers are there to reach new audiences? How can you ensure that a bigger audience doesn’t mean fewer conversions and less relevant consumers?

 

What is lookalike modeling?

This is where lookalike modeling comes in. Marketers need to find new customers and ensure that these new audiences are relevant to their businesses goals.

Lookalike modeling is the process of identifying new customers that look and behave like your current audience.

It involves taking a seed audience and defining key characteristics which differentiate these. From here smart modeling and other processes will help to identify a new larger, audience that is similar to your current customers.

 

What do you need to start building lookalike audiences?

As with many forms of digital advertising, lookalike modeling works using data. Data comes in many forms, and it’s really up to you to decide on which datasets are the most effective at identifying your target customer.

The most successful lookalike audiences are based on unique first-party data. This needs to encompass a range of first, second and third party datasets that cover both online and offline behavior.

That’s an awful lot of data to process, notwithstanding the process of collecting processing and managing that comes along with it. Luckily there are several solutions to help.

DMP for lookalike audiences

This data is combined with a program that can quickly identify other consumers who exhibit similar behavior. This process usually occurs inside a DMP (data management platform). It can also be done in some demand-side platforms (DSP) as well as in house.

in a little box – a Data management platform is a tool that aggregated and unifies data from many different sources to create a clear, holistic view of your data.

 

How does lookalike modeling work?

If that sounds slightly complicated, do not worry. Lookalike modeling is simple as long as you have the right dataset to work from.

 

Choosing datasets

First party, second party, third party, online, offline CRM, purchase, location – data comes in many different forms and comes from many different places.

You need to pull these datasets into a single place to maximize the effectiveness of your lookalike audiences.

This data is essential to get right. The more information you have, the more likely you are to build a better lookalike audience.

 

Define attributes

Next up you’ll need to identify the attributes or behaviors that identify your most valuable customers.

This will look different depending on the type of data sets you’re using. You can combine attributes from different datasets to create more specific seed audiences.

The more specific your look-alike model, the more likely you will find your target audiences. The stricter your seed audience, the more likely it will help you to realize your goals.

Of course, this will affect the size of your lookalike audiences. The more attributes you select, the more likely you are to filter out potential customers.

Ultimately it depends on the goals of your campaigns and what you want to achieve by building lookalike audiences. If you need to target specific people with a high-value proposition, then it might make sense to use more narrowly defined behaviors.

However, if you are looking to focus on reach and awareness then being less strict with your attributes will generate a larger audience that will most likely drive more awareness.

 

Some examples of datasets and attributes

Location-based lookalike audience

Purchase data

frequency and amount

Browsing history

Interest in specific products

 

Building the lookalike audience

This is done in the DMP or DSP and will look slightly different depending on the type that you use.

For external lookalikes, this might be done via a third party. For example, location-based lookalikes will usually be done by the provider.

The process is similar depending on where it occurs and look like the following.

 

  1. Analyze the seed audience
  2. Apply algorithms to find profiles that match
  3. The result is a lookalike audience

 

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What can you use lookalikes for?

The primary use for lookalike modeling is to find new prospects for your business.

Building lookalike audiences allow marketers to scale their campaigns to relevant consumers. With the instant reach available to marketers via digital targeting platforms, lookalike modeling can instantly help a business scale their key metrics and improve their bottom line.

Lookalike targeting can also help to extend the reach of specific campaigns. All campaigns eventually run dry, no matter how effective they are. Using lookalike audiences, these high performing campaigns can be extended to reach new audiences that will hopefully have a similar level of conversion.

Audience modeling is part of every successful media buying strategy. All media buyers should be aware of how lookalikes work in order to make informed decisions concerning their ad campaigns.

 

Best practices to build lookalike audiences

  • Find the line between reach and conversion – you need to focus on the number of attributes that you select. Too many might reduce the reach of your lookalike. Too few and your lookalike audience will not be closely related to your seed audience to produce the desired results.
  • The more data, the better the lookalike modeling will be
  • Think about new datasets that your competitors aren’t using. This will give you an advantage and allow you to build better lookalike audiences.

Related: read more about how location-based audience segments are built.

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Audiences & Segmentation

The Future of Personalization – Data Personlized Marketing

Personalization is one of the most exciting areas in the world of advertising and marketing. Today’s consumers expect a much higher level of customization, with companies like Netflix and Spotify raising the bar in terms of what the average consumer expects.

In a year with the advent of GDPR – it’s reassuring to realize that personalized marketing and advertising can be done in an intelligent and insightful way. This is all possible while complying with privacy legislation.

Customer data and insights are at the heart of the future of personalization. We’re beginning to see the benefits of bringing vast amounts of data together to asses analyze and make the right, informed decisions.

For businesses, this translates into a more personalized marketing strategy, product personalization and the ability to adapt to ever-changing trends.

 

What is personalization?

Lack of contextual understanding for consumers’ behavior has long held back the effectiveness of personalization in spite of a wealth of data, but marketers are finally starting to get a grip on it.

Consumers are demanding more personal experiences, and everyone from retailers to advertisers, marketers and product designers now understand the benefits that personalization can bring for their bottom line.

A lack of context around consumer behavior has previously limited the level of personalization available. Data has increased, but actionable data has often been harder to identify.

As datasets have improved, businesses have become better at understanding what makes good data and how they can use this to fuel cutting edge innovation in personalization.

This ultimately provides better marketing, improved one to one experiences and the ability to predict trends and consumer needs to deliver personalized experiences across the consumer journey.

 

Understanding your business is the first step of personalization

Personalizing the consumer experience first involves understanding your business. You have to know who your customers are. You have to know what they look like, what they like to do and how they behave in different contexts.

 

Understanding the context of engagement

The first step involves understanding the context of engagement. Personalization has improved, but with some datasets, context can be hard to discern.

Without this understanding of context business risk poor personalization that consumers will reject and struggle to engage with.

Building a detailed view of how your customers use your products, engage with your various touch points and illustrates why they are doing this will provide a solid base for highly effective personalization strategy. It’s also a great case for POS integration, helping you to get a unified view of ever point of customer interaction.

An example of this involves combining data to create a holistic view of your customers. If you are looking at personalizing your brand marketing, it’s not just enough to identify that a consumer fits within the profile of your target audience.

They might not be in the right frame of mind for engagement. Combining profile data with other data sets that can signify intent is a much better way to achieve great personalization.

For example, combining profile data with precise visits data to similar categories of a store can help you to understand the context. From here it’s possible to create highly personalized communication based on real-time consumer behavior.

 

Understanding your area and target audiences

It’s essential to maintain your personalization strategy so that as things change, you can adapt your personalization strategy.

If you have a physical consumer touch point, changing trends in your area can occur quickly. Understanding these changes can give you an advantage over other brands and retailers in the area.

Visits data combined with demographic data can help to identify who visits your store, your competitor’s store, the area and where they come from.

For example, identifying that Chinese nationals visits to the area are growing month on month can be valuable for your physical retail personalization strategy. You can personalize your retail environment to drive revenue and visits.

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Fully personalized marketing strategies

Offers, incentives and one to one marketing in retail

The challenge with many marketing strategies is that offers, promotions, and incentives are developed to be one size fits all. Many retailers, for example, will have a single offer aimed at every store visitor.

But each consumer is unique with different personalities, profiles, motivations and brand history. Personalization is valuable in these instances as it helps to deliver the desired offer to the relevant customers.

The aim with marketing personalization is to get get the ideal offer which is most likely to convert a specific customer to the customer is the right moment.

Data is enabling this process already. But using intent data such as consumer location or interaction history and matching this to the ideal offer is improving the level of personalization marketers can deliver.

By combining intent data with other datasets such as store visits or purchase data, retailers can see how each offer affects purchases. This combination means that marketers can understand the impact that each offer has physical store visits or purchases.

With data, retailers can begin to respond to each consumer at an individual level. The data that they use to achieve this will help them to simultaneously optimize these offers and the delivery of these offers to improve their marketing and their bottom line.

 

Media buying & personalization

It’s hard to talk about personalization without focusing on digital marketing personalization, and more specifically media buying.

For paid media, the ultimate goal is to achieve a one to one marketing strategy. With the rise of technology, it’s now easier and quicker to deliver personalized marketing at scale.

New datasets have developed a deeper understanding of consumers and how they behave in both the online and offline worlds. Using data allows brands to reach consumers with personalized marketing, across many different channels and touchpoints.

Understanding where and how consumers move can help brands to personalize their marketing activity. Location-based segmentation, for example, allows marketers to build more specific audiences, optimize ROI and reduce wasted ad impressions.

Media buying platforms offer many ways to segment audiences, but a rise in unique third party datasets have meant that marketers can segment and fine-tune audiences better than ever before.

 

Predictive personlization

The data that marketers now have at their disposal has enabled them to do more than just personalize based on past consumer behavior.

Advanced datasets can take personalization to the next level. Marketing personalization is becoming predictive. Brands and advertisers can now combine multiple data sources to understand how consumers behave on both a micro and a macro level.

Using this information, it’s possible for marketers to become predictive with their personalization.

Marketers can continuously update their perceived customer profiles with data that explains a consumers profile clearly. This helps the business to personalize the consumer journey and remove potential barriers to purchase.

Data is enabling businesses to understand which areas to invest in the future to deliver personalization.

 

The personalization power of unified datasets

As we’ve already alluded to – the future of marketing personalization doesn’t just involve a single dataset. It’s the combination of many which will bring new levels of personal marketing and brand interactions.

As data increases the trend for unified datasets will do so as well. To create high levels of personalization we need to find an effective way to consolidate the data sets that can fuel personalization.

Data platforms are catching up with the personalization needs of the modern marketer. The infrastructure is advancing to support the staggering growth of data that is available for marketers to drive personalized marketing efforts.

The data is useful to drive marketing personalization, but it will soon extend beyond this into other areas of the business. Data platforms are delivering highly personalized marketing to customers, but they are also having an impact in other areas such as logistics, the supply chain, and product development.

Related: read more about how location-based audience segments are built.

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Audiences & Segmentation

Tamoco & Audiens Bring Location Segmentation To Ad Platforms

Tamoco announces new partnership with Audiens to deliver precise location-based segments in leading ad serving platforms

  • Tamoco’s location segments will become instantly available in leading marketplaces.
  • Partnership will help advertisers target consumers more effectively through precise location-based services.
  • Partnership will make it easier for advertisers to access Tamoco’s location data through platforms such as The Trade Desk, Adobe or Facebook.

Tamoco, the world’s largest proximity network, has today announced a new partnership with Audiens, the worlds easiest to use customer data platform (CDP). The partnership allows advertisers to gain more precise insight into their audience members through their existing ad-serving solutions.

This partnership makes Tamoco’s location segments instantly available in leading platforms such as DoubleClick, AppNexus and Adform. It also makes it easier for advertisers to access Tamoco’s location data through platforms such as The Trade Desk, Adobe and Facebook.

Tamoco have 1 billion proximity sensors worldwide, collecting data on over 100 million devices, with more than 4 million MAUs in the UK. The product enables businesses to build better products, understand audiences and make better business decisions by using powerful mobile device data. Testament to the success of the product, Tamoco has previously worked on projects with industry giants including Uber, Heineken and The Coca-Cola Company.

“What this partnership represents is an opportunity for brands to understand their target audience’s habits and movements with greater accuracy than ever before.” Founder and Executive Chairman of Tamoco Sam Amrani said. “We’re thrilled to be working with Audiens and we’re really passionate about the difference that this partnership will bring to the industry and to our clients.”

As a result of the partnership, advertisers can now reduce campaign wastage and target consumers effectively by using more precise location-based audiences.

“The move will allow savvy brands and agencies to more accurately fine-tune their campaigns based on how consumers behave in the offline world,” Amrani said. “This creates more specific and customizable segments which can be used to further personalize advertising.”

The Audiens CDP unifies complex data across websites, apps, in stores, CMSs, CRMs and other data sources. It builds meaningful audience segments and conveys easily-understandable insights, specific to the marketing needs of a business and brand.

“Our clients demand the very best in location data and the partnership with Tamoco ensures we can deliver accurate, relevant and scalable location segments,” said Marko Maras, CEO, Audiens. “It adds another premium partner to our data marketplace, enabling our customer data platform to reach new global markets”.

 

About Tamoco:

Tamoco is making powerful location data accessible for all. Its global network provides businesses, organizations, brands, developers access to the leading source of precise, real-time location data. Tamoco is enabling businesses to build better products, understand audiences and make better business decisions by using powerful mobile device data.

 

About Audiens:

Audiens is a Customer Data Platform (CDP) that creates a persistent and unified customer database to improve the performance of advertising campaigns. Customers’ data can be onboarded from multiple sources (website, mobile app, CRM), normalized, and combined to create advanced audience segmentation. This structured data is then pushed to the most popular digital customer acquisition channels and marketing networks. Audiens makes it very easy for marketers to activate customer segments privately, or share them with other advertisers for data monetization.

Related: read more about how location-based audience segments are built.

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Audiences & Segmentation

Using Location Audience Segmentation Directly In Your DMP

Why location is important

Advertisers use audience segmentation so that they can eliminate any unnecessary spend from their marketing efforts, become more efficient and effective and boost key KPIs.

Using data, advertisers can create much smarter audience segments. They can prioritise the right consumer, with the right ad using the right message.

That’s why we’ve made sure that our precise, first-party data sets are available for marketers and advertisers to use directly in their DMP. Advertisers should be able to do this directly in their media buying solution.

 

About our data

At Tamoco we think that data accuracy is the most important thing for advertisers using data in their targeting, segmentation or attribution. That’s why we built a network that is leading the way in the drive towards more accurate data.

 

Proprietary location data focused on precision and accuracy

Tamoco’s data is industry leading. We use our proprietary SDK for data collection along with our extensive network of sensors to understand consumer location with higher levels of precision and accuracy.

 

Detailed visit behaviour

Tamoco’s data methodology is designed to reduce the number of incorrect data points. We understand visits with granular accuracy. False visits are filtered out, and our data methodology is transparent. This methodology means that advertisers can be confident that Tamoco visits data is more accurate than other visits-based targeting solution.

 

Benefits of using location directly in your DMP audience segmentation

Fine-tune audiences and incremental ROI gains

You have the first-party data which contains, for example, age, gender, brand loyalty and products owned, amongst others. Location allows you to segment these audiences even further.

To drive incremental gains to ROI location can signify which of these users are relevant to your campaigns. Location data is real-time and behavioural based. These attributes mean that you can exclude irrelevant audiences, and save valuable marketing dollars in the process.

Advertisers can then tailor their campaigns to users that have physically exhibited certain behaviours, such as visited a specific store or frequented a series of physical locations.

This targeting helps to build relevant messaging and ensures that your segments are squeaky clean in terms of precise targeting. No more wasted budget on consumers that aren’t relevant to your brand campaigns.

 

Location is a good indicator of intent

Add intent to the segmentation process. Retargeting campaigns are more effective if you can reach consumers when they are in the right frame of mind. Retargeting works well in the online world, but this is often limited to your current inventory.

Consumers show purchase intent in the offline world as well. Visiting your store is a good example. However, by mapping the offline world, advertisers can use location to identify consumer intent in different ways.

Retargeting to consumers who have visited (or are currently visiting) a competitor or a store in a similar category is a powerful way to reach the right audiences.

 

Using location data to fuel analytics

Using location signals directly in your data management platform enables smarter cross-selling. If you have built up a database of descriptive and behavioural audiences, adding location can provide a better way to upsell new products or promote return purchases.

Using your analytics solution, location data can directly increase how you understand consumer trends, patterns and intent. Location data is a tool for building up a more detailed view of your customers.

These insights can be used to inform future segmentation and predict which audiences are more likely to convert at a specific stage in the buyer journey.

 

Using location to build lookalike audiences

Reaching new customers that are currently outside of your customer data set can be challenging. It’s something hard to know if the process of building lookalike audiences is reliable.

Using location data, it’s possible to build real-world behavioural based audience segments. For example, by taking an audience that converts highly, we can understand similar consumers based on how they move and behave in the real world.

This generates lookalike segments that are based on current real-world behaviour rather than vague similar interest data. Ultimately it will build segments that are more likely to convert.

 

Example segments and audience segmentation strategies

Some of our location-based segments are available already. Here we will look at some familiar audiences segmentation use cases using this data.

 

Women’s clothing stores

Brands looking at segmenting their audiences based on consumer interests can use location to refine their audiences. Let’s look at how this would work with an audience based segment.

You already have a pre-built audience that is relevant to your women’s clothing brand.

Using location-based filters directly in your DMP you can further filter this audience to reach the most relevant users.

You can filter based on the number of visits to women’s clothing stores. You can set the time period for these visits.

This will segment your audience based on those that have physically visited a clothing store in your defined time period.

 

Drinking places (alcoholic)

Using location, you can build retargeting audiences in your DMP to maximise your ad budgets.

If you are looking to retarget consumers based on their behaviour, then location can help to define the right audience.

Set your audience to include those that have visited an alcoholic drinking place.

You can filter these visits based on past visits, or on specific dates or days of the week.

This can help you to build incredibly specific audiences – such as Friday night venue attendees.

 

Speciality food stores

As previously mentioned, location data can be helpful to build new lookalike audiences based on consumer behaviour. This method can help you to create unique lookalikes based on actual measured real-world behaviour.

We can build an audience based on visits to speciality food stores. Here we have our seed audiences that consists of consumers that we know have visited a health store at some point in our defined time period.

We can do one of two things here:

  • Move the identifiers into our current lookalike modelling solution. This will create a new audience based on a unique seed audience.
  • Use a location-based lookalike solution. This will use the audience to match with devices that have exhibited similar real-world behaviour.

All of the above segments are readily available in leading DMPs and other media buying solutions.

 

How to activate Tamoco’s precise location data

Our data is currently available through DoubleClick, AppNexus, AdForm. Here you can begin segmentation immediately using Tamoco’s location data.

We can activate these segments instantly in The Trade Desk, Adobe Marketing Cloud, Facebook Advertising, Sizmek, Beeswax, Widespace and BrightRoll. Please contact us to enable this.

 

Want something more custom?

We can build custom segments on demand with our team of data scientists. These can be fed into the above solutions. Here are some examples of what our team can provide for your campaigns.

Brand affinity – we can create segments that are based upon brand affinity to your brand, a competitor or another relevant brand.

Detailed visits – Our team can help segment audiences based on verified visits to any physical POI, venue or location.

All of our data solutions can be fed into your current data or targeting platform. Our team of data scientists are ready to support your integration and take your marketing to the next level.

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Related: read more about how location-based audience segments are built.

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Audiences & Segmentation

Using ASO to Grow Your Mobile App Audience

Your mobile app has launched or is getting close to it. This is excellent news, but the work doesn’t end there. Teams must also find ways to grow the app’s visibility and get users. The key to growing a mobile app’s audience is through App Store Optimization (ASO). When putting together an ASO strategy, it’s critical that teams remember several key points, including:

  • Using mobile data
  • Understanding how the algorithms work
  • Selecting relevant and high-volume keywords
  • Developing appealing creatives
  • Testing their app pages
  • Launching paid campaigns

 

The Importance of Using Mobile Data

Mobile data is essential when putting together a solid ASO strategy. This is because there is only a 20% overlap between web and mobile keyword volume estimates. What this means is that using web data to guide an ASO campaign can sabotage it, leading to wasted resources and efforts.

Developers may be tempted to use free tools that look similar to Google keyword planners, but many of these use web keyword scores, which don’t translate well for discoverability in the app stores.

 

What Users Search For

Users search differently on the web than they do on the app stores. App store search is feature-driven, rather than research-based. For example, while a user might search for “How to edit a PDF?” on the web, they would instead search “PDF converter app” on the app stores.

When you prepare an ASO strategy, you’ll need mobile data from an ASO platform like DATACUBE. Otherwise, your data will not correctly reflect the terms users are searching for, misguiding your strategy.

With the right mobile data, you can target the proper terms that will help your app’s audience grow.

 

How Each Stores’ Algorithm Works

Mobile data is only a piece of the ASO puzzle. Understanding how the app store algorithms work is also necessary. This is because the Apple App Store and Google Play Store crawl metadata differently, requiring different approaches. One thing they do have in common is that they both look at an app’s click-through-rate (CTR) when determining rankings.

How the Stores Index Your App

On iOS, Apple’s algorithm will crawl your app based on keywords in the:

  • Title
  • Subtitle
  • Keyword Bank

Apple merchandises apps based on their targeted keywords when determining what phrases to index it for.

For Google Play, the algorithm looks at the:

  • Title
  • Short Description
  • Long Description

There is no declared keyword bank on Google Play. Instead, Google Play’s algorithm examines the metadata fields from left to right, top to bottom and pulls keywords and phrases from there. The closer a keyword is to the front of a line or sentence, the easier it is for Google’s algorithm to pick it up.

Including keywords in prime areas will help with indexing on the Google Play. This will then increase your chances of reaching your audience who is searching for apps just like yours. This is what makes understanding stores useful to app growth.

 

Setting Up Your App’s Metadata

Once you have access to mobile data and have an understanding of how each app store works, you’ll then want to start researching the app’s space. When getting started, it’s essential to determine what keywords are high-volume and relevant. DATACUBE is an ASO platform that can help you research the keywords you want to target and decide if they’re aligned with your target market.

You’ll also want to research your competitors and see what keywords they rank well for, find related keywords you can target and see what performs best for other apps in your field.

 

The Metadata Fields

Apple provides users with the title and subtitle at 30 characters each, plus a keyword bank of 100 characters. It’s vital to use as much of that space as possible to have the maximised number of keywords.

Google Play has no keyword bank so that an app will rank for the keywords included in within its title, short description and long description. It’s essential to enter the keywords and phrases precisely as you want to target them, as the algorithm does not account for variations of keywords.

Make sure the title, subtitle and descriptions are readable and not keyword-stuffed as this could hurt conversion. If your app uses the right keywords correctly, you’ll have a higher chance at reaching and converting more users, ultimately growing your audience.

 

Designing Creative That is Optimized for Conversion

Click-through-rate is also crucial for improving an app’s keyword rankings as well as measuring conversion. Given that 70 per cent of installs come directly from search, an app’s search presentation should be appealing, helping encourage users to install it.

Screenshot Best Practices

An app’s screenshots should include call-to-actions that utilises high-volume keywords to inform users of the app’s features. The text should be concise and digestible, quickly getting to the core message while using popular keywords.

The screenshots should be up to date and optimised for the devices they’re viewed on including image upscaling if necessary. Outdated screenshots can look poor when viewed on devices with better image quality, turning potential users away and hurting conversion.

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Screenshot Guidelines

Apple allows up to ten screenshots, while Google Play allows up to eight. The app’s listing should utilise each of them to provide as much information about the app and its features as possible. The screenshots on iOS can also be used for Search Ads A/B Testing to determine which creative elements convert best. By launching two search Ads campaigns with different creative sets, you can compare the results to determine how well each set drove conversions and apply that information to the app’s live listing.

Preview videos are another way to help increase conversion. Apple and Google Play each allow videos, although the guidelines are different for each store.

Videos on the Apple App Store can:

  • Only show in-app footage
  • Must be between 15 to 30 seconds long.

The Google Play Store’s videos can:

  • Link directly to YouTube
  • Run any length, but should be between 30 seconds and 2 minutes long
  • Show content from outside the app

 

A/B Testing

A/B testing is important for determining how effective creatives and metadata are. ASO is an iterative process, and to continue growing, it’s critical to understand that what worked a year ago, may not work today. A/B testing can help your team find what’s working now, helping it stay relevant and grow.

Splitcube is an app A/B testing that emulates the app stores, allowing teams to test creative and keywords. Splitcube can also heat-map user behaviour, tracking how users search and which apps, and elements, they are most likely to click on. As your creatives are important for converting users, testing them before deployment helps ensure the ones you use will help grow your mobile audience best.

 

Paid Campaigns

Reaching as many users as possible is key to increasing your audience. Paid marketing campaigns help a wider audience see your app through advertisements and paid placement in the search results.

Paid campaigns can include:

  • Search Ads
  • Google Ads
  • Social media marketing

To improve your audience within the App Store, Search Ads is essential.

Utilising a paid campaign like Search Ads will place your app at the top of certain search results. You can select the keywords and demographics you’re targeting to help your app reach the audience you want to see it. Targeted advertisements can help put your app front and centre before users who are most likely to be interested, which can be made even more effective with the use of creative sets.

The first three apps in search results receive about 41% of the clicks from those searches, making them valuable spots to be placed in. With paid campaigns, your app can appear in that top spot, which can help increase the number of users that view your app and convert. Search Ads boasted a 50% conversion rate and average CPI of $1 in 2017, making it an affordable and efficient form of paid marketing.

Paid marketing campaigns also help improve your organic indexation. Running campaigns such as Search Ads can increase your click-through-rate, which will help improve your app’s rankings within the store to reach and convert even more users.

 

Conclusion

App Store Optimization makes it possible to grow an app’s audience continually. This involves:

  • Using mobile data
  • Understanding how each stores’ algorithm works
  • Researching metadata
  • Designing optimised creatives
  • A/B testing
  • Utilising paid campaigns

By taking this approach, teams can help their app consistently grow and thrive.

This is of course only the tip of the iceberg for App Store Optimization. ASO is an iterative process, one that requires consistent upkeep and revisions to keep up with changing consumer trends and developments.

To continually expand a mobile app’s audience, one must revisit their ASO strategy on an ongoing basis. By continuing to iterate while using best practices, teams can ensure they are providing the best outcome for their app’s growth.

Related: read more about how location-based audience segments are built.