Strategic Consulting Services

Unlocking the Power of Your Marketing Data

Grant Simmons — Tue, 28 Nov 2023 16:02:50 +0000

What Kochava Foundry Can Do for Your Brand

In today’s digital age, data is the unsung hero behind successful marketing campaigns. It’s like the wizard behind the curtain, making the magic happen. Brands need accurate, timely, and trustworthy data to make informed decisions, optimize their advertising efforts, and connect with their target audience effectively. Enter Kochava Foundry, our trusty sidekick, here to help you harness the power of data with a bit of a wink and a nod. In this blog, we’ll explore what Foundry can do for your brand, all while sneaking in a dash of wry humor.

1. Data Source Validation: Separating the Gems from the Cubic Zirconia

Let’s face it: not all data sources are created equal. Some are as reliable as your GPS, while others might lead you down a rabbit hole. Foundry starts by doing what we like to call “data source validation.” We’re like the data bouncers checking IDs at the door. We ensure that your data sources are the real deal—accurate, complete, timely, and as secure as a secret agent’s briefcase.

With Foundry on your side, you won’t have to worry about data that’s faker than a spray-on tan. We’ve got your back, and we won’t let your brand fall victim to unreliable data.

2. Data Quality Assurance: Polishing Your Data Crown Jewels

Data quality is the crown jewel of marketing success. It’s like having the Hope Diamond in your marketing toolkit. Foundry takes data quality seriously, making sure your data shines brighter than a supernova. We perform meticulous data quality assurance checks to spot any data blemishes or imperfections. Think of us as the data beauty therapists, making sure your data looks flawless.

3. Timely Data Delivery: We’re Not a Pizza Delivery Service (But Close)

In the world of digital marketing, timing is everything. Foundry ensures that your data arrives on time, every time. We understand that delayed data is like cold pizza—nobody wants it. So, rest assured that your data will be as punctual as a Swiss watch.

4. Data Security: Better than Fort Knox for Your Data

Security is our middle name (well, not really, but you get the point). We treat your data like it’s a national treasure. Foundry takes stringent measures to protect your data during its journey, making sure it’s secure every step of the way..

5. Data Source Reviews: Tea Time with Data Providers

Foundry goes the extra mile by establishing a tête-à-tête with data source providers. It’s like having tea time with your data buddies. We keep the lines of communication open to address any data-related issues promptly. We’re like the friendly neighborhood data watchdogs.

6. Actionable Insights: The Sherlock Holmes of Data Analysis

With Foundry, you gain access to actionable insights that Sherlock Holmes himself would envy. We help you decipher data, spot trends, and make data-driven decisions. Think of us as your trusty Watson, guiding you through the mysteries of your data.

7. Compliance and Industry Standards: Staying on the Right Side of the Law

We make sure your data sources play by the rules, just like a stern school principal. Foundry helps ensure you understand compliance with industry standards and regulations, keeping your brand out of hot water.

Foundry is Your Data Superhero

In summary, Foundry is your data superhero, here to help you make data-driven decisions with a hint of wry humor. Don’t let your brand’s success be left to chance—partner with Foundry, and let’s embark on a data-driven adventure together.

Reach out to us today to learn more about how Foundry can bring a touch of levity while we help supercharge your data management efforts. After all, who said data had to be boring?

The post Unlocking the Power of Your Marketing Data appeared first on Kochava.

Navigating the Ad Spend Jungle

Grant Simmons — Tue, 24 Oct 2023 18:53:35 +0000

How Insight Packs from Kochava Foundry Light the Way

In the intricate landscape of digital marketing, brands confront a plethora of challenges. These obstacles, ranging from misattributed user acquisitions to the ever-changing ad realm, often leave advertisers in a perplexing situation. How can they confidently allocate their precious ad spend, knowing they’ll be scrutinized over the final outcome (success or failure)?

Kochava Foundry, with its revolutionary Insight Packs, emerges as a beacon, guiding brands to make informed and impactful decisions based on expert analysis.

Deep Dive into Insight Packs

Foundry, always at the forefront of innovation, offers two trailblazing Insight Packs tailored for today’s marketing conundrums:

Incremental Intent: In a world where every network claims superior customer acquisitions, Incremental Intent emerges as the truth-seeker. By meticulously calculating the variance between organic and driven installs, this tool offers a crystal-clear perspective. Brands can, therefore, redirect their budget towards avenues that genuinely amplify their advertising impact.

Loyalty and Engagement: The modern consumer is discerning and volatile. Retaining their loyalty is a Herculean task. This Insight Pack offers a magnifying glass into your media strategy’s real impact. By highlighting how different channels and campaigns influence customer loyalty and engagement, brands get a roadmap. Following this, they can judiciously adjust their spend, maximizing ROAS and fine-tuning acquisition strategies.

Dissecting and Addressing 10 Key Pain Points

Let’s delve deeper into key marketing challenges and explore how Kochava Foundry’s tools and expertise pave the path to solutions:

1. Attribution Confusion:
Our sophisticated attribution platform delves beyond surface-level data. By leveraging both deterministic attribution and probabilistic modeling, we ensure an unambiguous view of user acquisition sources. Brands can then confidently reward the deserving networks for the conversions they actually drove.

2. Suboptimal Ad Spend:
The Incremental Intent Insight Pack stands out as the sentinel guarding against wasteful ad spend. By distinguishing between organic and campaign-driven acquisitions, it provides a nuanced understanding, helping brands streamline their budgets for optimal impact.

3. Low Customer Engagement:
Our advanced engagement analytics dive deep into user behavior post-installation. When merged with insights from the Loyalty and Engagement Insight Pack, brands receive a comprehensive view of any discrepancies. This enables a recalibration of ad messaging and the user experience to better align.

4. Short-term User Retention Woes:
Our retention analytics meticulously chart out user behavior trajectories post-install. Brands gain unparalleled clarity on user drop-off points, enabling them to refine onboarding and engagement touchpoints.

5. ROI Uncertainty:
Our detailed ROAS reports break down the performance of networks and campaigns, segment by segment. This granular view empowers brands to discern the genuine high-performers, ensuring investments that promise tangible returns.

6. Over-reliance on a Few Networks:
Our exhaustive performance metrics catalog offers a panoramic view of multiple networks. Brands, thus, are nudged to venture beyond their comfort zones, discovering uncharted territories in the advertising world.

7. Lack of Actionable Insights:
Kochava Foundry transcends traditional data offerings. With a blend of strategic consultations and expert-backed recommendations, brands receive a clear, actionable blueprint for the future.

8. The Ever-Changing Ad Landscape:
At Kochava, we pride ourselves on our agility. As digital advertising undergoes metamorphoses, from privacy regulations to emerging platforms, we ensure brands aren’t left in the lurch. With timely guidance, integration advice, and adaptive strategies, brands remain ahead of the curve.

9. Siloed Data Interpretation:
Our holistic dashboard amalgamates diverse metrics, offering brands a cohesive narrative. This unified perspective, enriched with data visualization tools, ensures brands grasp the intricate dance of different metrics and their cumulative effects.

10. Long-term Strategy Struggles:
We believe in a 360-degree approach. By synergizing historical data insights with forward-looking predictive modeling, we ensure a brand’s short-term tactics seamlessly merge with its long-term visions.

Insights for the Dynamic World of Digital Marketing

In the dynamic world of digital marketing, a brand’s survival hinges on its adaptability and informed decision-making. With Foundry’s Insight Packs, brands are equipped with a compass and a roadmap. As they navigate the tumultuous terrains of the digital realm, Kochava ensures their journey is not just safe but also supremely successful.

Visit Kochava.com/Foundry-Insight-Packs/ to learn more about Insight Packs and request a free consultation.

The post Navigating the Ad Spend Jungle appeared first on Kochava.

Gain In-depth Insights From Your Data with Kochava Foundry Strategic Services

Leslie Amadio — Thu, 17 Dec 2020 21:19:41 +0000

Kochava Foundry^TM provides custom analytics and recommendations for today’s competitive marketing environment

Adtech is a complicated and highly competitive environment, and with increased data privacy regulations, marketers need to make the most of their campaign data. While traditional measurement tools have their place and value, periodically, it is beneficial for brands to dig deeper into their campaign data and glean insights for new directions and evidence-based marketing decisions.

Kochava data analysts have been doing just that for the past several years and have consolidated their Strategic Services under Kochava Foundry^TM to offer marketers customized reporting and recommendations for optimization, fraud mitigation, and brand growth. The team is composed of analysts with backgrounds including retail to academia where they are able to bring best practices from their respective areas of concentration. The team is adept at analytic deep dives to investigate irregularities and provide recommendations for optimization.

Advanced Measurement and Valid Incrementality Testing with MediaLift

Real-time analytics alone are not evidence enough of the true impact of ad campaigns. To answer that, incrementality testing—an often costly and time-intensive exercise—must be performed.

The Kochava Foundry team, led by Grant Simmons, Vice President of Kochava Foundry, is an industry expert on incremental and advanced measurement techniques. Traditionally, the holdout group is carved out of the target audience, and marketers must withhold displaying ads at the cost of losing potential revenue. Grant and his team have devised a method to model comparable forensic control (holdout) groups and by doing so, reducing the time and cost to determine incrementality.

Devices in the test audience segment are matched and scored (based on several characteristics) and then paired against similarly scored devices in a forensic control. The team can then measure the incremental lift impact between the test segment and forensic control. This methodology means the marketer doesn’t have to sacrifice displaying ads to a portion of their audience for holdout purposes or worry about unwanted bias that is often present between traditional test and control segments.

MediaLift services also include in-depth analysis of a campaign’s reach vs. the frequency of ads displayed. The team also looks beyond the quantity and installs and events and determine the quality of conversions to measure true performance.

With iOS 14 limiting access to device ID and related data, marketers may tap into other advertising channels, such as, out-of-home (OOH) and digital out of home (DOOH), and over-the-top (OTT) and connected TV (CTV). These channels have already been increasing in use and the team can tie them back to mobile data for a holistic view of all ad campaigns.

Marketing optimization and managed services

With real-time data, it’s easy to focus on the moment and miss what’s happening within the bigger picture of your campaigns and marketing strategy. For instance, the industry’s attribution last-click standard awards only the partner with the last click, and it’s easy to oversee networks and publishers’ sites that were valuable influencers. Kochava Foundry can determine valuable channels, reduce inefficiencies, and help you understand how conversions are influenced for optimization moving forward.

The Foundry team will also help optimize by analyzing an app’s unique traffic and recommend verification and attribution customizations to fine-tune marketing credit and mitigate against non-relevant traffic.

The team recently did a custom analysis for a television network whose ultimate goal was to determine what types of content to create next. With so much noise in the ecosystem and money at stake, the Foundry team identified which media partners were valuable influencers, eliminated overlap in the client’s media mix, and recommended lookback windows to show where the most conversions were occurring.

Fraud Audit

In addition to recommending traffic settings to prevent fraud, the team has performed campaign audits for both customers and non-customers where they identify the types of fraud present in campaigns and recommend steps for mitigation. Marketers can sign up for a free fraud assessment and see how the team can help prevent fraud in future campaigns.

Take advantage of our experts

The Foundry team has performed in-depth analysis for some of the biggest brands and Fortune 500 companies in advertising with some brands returning quarterly or annually for their data deep dives. For more information about Kochava Foundry, visit https://www.kochava.com/foundry.

The post Gain In-depth Insights From Your Data with Kochava Foundry Strategic Services appeared first on Kochava.

CPI on the Rise? Your Own Strategy may be to Blame

Kevin King — Mon, 06 Jan 2020 23:46:04 +0000

Why clean and clear target audience segmentation is important across partners.

How well are your network partners working for you?

In a perfect world, you run campaigns with multiple networks to cast a wide net and improve overall reach. The goal is healthy acquisition of unique, high quality users at an efficient cost per install (CPI). However, if you’re not being specific enough in your targeting segmentation, heavy overlap in your media mix may be inadvertently driving up your CPI.

A crowded media mix leads to high CPI

User acquisition is far more complex than direct return on ad spend. If it were that simple, marketers would pile their spend into their best campaigns. As it is, when networks overlap, targeting the same user in the customer journey, not only is the user experience more likely to suffer from oversaturation, it can also result in network partners receiving confusing postback signals for optimization.

Suppose a particular user lands in the same target audience criteria (females, US, 18-25, casual gamers) for campaigns across three different partners. Each partner correctly targets the user and serves them an ad. Network A serves a 30-second playable that captures the user’s attention. The user goes back to playing the game they were already in, but a positive imprint has been made that they want to try this game later. An hour later, Network B serves them an ad on mobile web, while the user is reading an article. They don’t click on the ad, but go to the app store and search the game, at which time Network C serves a Google Search ad and the user clicks it.

With fractional attribution, marketers would be able to distribute credit with various weighting based on influencer positions (ie, winner = 75%, first influencer = 15%, second influencer = 10%). While Kochava has long supported this model of attribution, industry adoption is stale and thus, we’re stuck with the “last touch takes all” model. The winner (C), gets paid for their targeting, but the networks that had influence touch points (A and B) get install postbacks informing them they lost. By losing attribution, they will optimize away from your target audience (even though in this case, the targeted user became a customer!) and their CPI will rise because they believe they have to serve more ads to obtain users.

You as the marketer only pay the CPI bounty to Network C. However, the efforts of networks A and B should not be mistaken as being “free.” When multiple networks generate impressions and clicks on the same user, all networks except the attribution winner believe their targeting was unsuccessful and will try not to serve impressions to users like that of your new user. The more this happens, the more impressions it takes to generate an install or action, thus lowering your eCPM and reach, while increasing your CPI. You’ll also apply this flawed logic to your ongoing marketing strategy.

Beware of overlapping with the walled gardens

This same conundrum becomes even more costly when it’s done with the walled gardens, also known as self-attributing Networks (SANs). SANs include Facebook, Google, Twitter, Snapchat, and several other major ad platforms. These partners play by their own rules when it comes to attribution. They charge per click (rather than CPI) and claim the installs that occurred on their platforms. So, instead of mobile measurement providers like Kochava notifying them whether they won attribution, it’s the other way around. They inform Kochava and others what they won.

When a SAN claims an install and conflicts with a mobile measurement provider’s (MMP) determined attribution winner, the marketer is stuck paying at least twice for the same install. They’ll pay the non-SAN network the CPI attributed by their MMP. However, they’re still billed for the clicks it took to drive the install on the SAN.

In the example below, two SANs claimed the same install in addition to the network that Kochava identified as the attribution winner—in this case, that means paying three times for the same install. This can be avoided with the right tools and information.

You want networks to deliver unique, quality installs of users who will perform downstream events.

How an overlapping media mix can cost you

Suppose a company wants to advertise their new app to 100,000 of their previous app’s customers. They upload this entire audience to four networks with which they’ve had prior success in user acquisition. Each network gets a $25,000 budget to activate as many of these customers as possible. Knowing that these are high value customers, the marketing team sets an internal goal of a $10 CPI or better.

Despite testing their creative in a soft launch, marketing toward prior customers, and working with proven user acquisition networks, the target CPI of $10 is exceeded. Why were the CPIs higher than expected, and why were the click-to-install rates far lower than average? The problem is found by looking to the table columns titled, “Lost SANs Claims” and “Lost Claims.”

SANs will claim install conversions for users who have seen and/or clicked on one of your ads through their platform, within their lookback windows. However, this is an incomplete view, as one of the other networks may have been more meaningful to attribution in terms of proximity to install or click vs. impression intent. Kochava reports installs that are claimed by a SAN, but end-up being attributed to another source, as a “lost SAN claim.” In Kochava’s standard attribution model, there can be only one winner, but in this scenario, the app developer will be charged by multiple ad networks for it.

To the non-SAN network that won attribution in Kochava, a CPI will be paid out. To the SAN that claimed the install in their own eyes, the target CPI is still paid through the cost per click on the campaign. Integrated networks will only claim an install when Kochava attributes one to them. That being said, this attribution deferral should not be mistaken for being “free” as it decreases the precision of targeting. In fact, you probably cost yourself an install for each of these overlaps. Why? Well, when an integrated network targets a user who saw or clicked on your ad, but did not receive the install credit, it is counted as a “lost claim.” When multiple networks generate impressions and clicks on the same user, all networks except the winning attribution now believe their targeting was unsuccessful and will try not to serve impressions to users like that of your new customer. This is not the type of feedback you want your network partners to optimize on.

In short, all four networks were focused on getting credit for bringing in the same users instead of acquiring as many unique users as possible.

Now, if instead each network was given a unique audience to target, the impression, click, cost, and click-through rates were far better. Without targeting overlap, lost claims were eliminated, and integrated networks did not lose claims on any installs they touched on attribution. This caused the install rate to nearly double, and CPIs to nearly half. Not every “lost SAN claim” or “lost claim” from the previous example is counted as a new attributed install, because some installs had three or four networks claiming against the same user. Due to the lack of overlap, the total marketing spend efficiency increased by nearly 50%.

Best practices to adopt

In an overcrowded media mix, the number of unique, quality installs decreases. If you’re not sure how much your media mix currently overlaps in targeting, we can help. You can visualize your media mix within Kochava through the influencer report. This report shows which networks had a touchpoint with a user before the last click was awarded.

Once you know how much you’re overlapping, these best practices to help you optimize in the future:

Avoid: Sending the same advertising identifiers for targeting to more than one SAN.

Solution: If you have a list of prior customers or power users, segment them by the network they were originally acquired from as that network has proven the ability to reach the user. Whenever targeting specific users on a network, make sure to blocklist those users from all other networks to prevent overlap. Once a network has failed to reach a user, then remove them from all blocklists and try a new network or multiple networks.

Avoid: Running multiple media partners who purchase inventory from the same providers.

Solution: Focus on a network’s targeting expertise to prevent overlap and improve the uniqueness of audiences. Let’s say two demand-side publishers (DSPs), Network A and Network B, have different specializations. Network A is known for having a large US audience, and Network B is known for reaching Android devices in Brazil. To avoid overlap, set your targeting preferences to each network’s region of expertise and consider negative targeting for the Portuguese language from Network A if they support it. Just because a network has incredible reach doesn’t mean you need to allow them to spend on untargeted run of inventory campaigns.

Avoid: Scaling by increasing partners and lowering CPI/CPA bids.

Solution: Ask account managers what eCPM is required to achieve the targeted impression reach, and then test and compare click-through and install rates with other similar partners. A high click-through rate may not be indicative of higher performance, as a low impression-to-click ratio can suggest click flooding. Low install rates suggest low-quality leads. If your app store page can convert browsers into users, ask why it would take hundreds if not thousands of user clicks to your page to get a single install. A “click” should measure intent, not be an attribution catch-all for less scrupulous networks.

The takeaway

Sometimes less is more. By adding additional networks to your media mix, you may cause overlap and decrease your overall performance. Kochava offers many tools, filters, and settings to prevent costly targeting overlap. Contact us to learn what overlap may exist in your media mix and how we can help optimize your future ad spend. The Kochava team can even do the heavy lifting for you, if you prefer.

Kevin King – Lead Client Analytics
Kochava

The post CPI on the Rise? Your Own Strategy may be to Blame appeared first on Kochava.

Avoid an Overcrowded Media Mix

Katie Darren — Wed, 15 May 2019 23:47:16 +0000

Today’s marketer is data-driven. More than simply connecting our clients with their data, Kochava seeks to empower strategic decisions through marketing intelligence. Our growing Client Analytics team is comprised of data analysts that work hand-in-hand with clients, delivering face-to-face business value assessments (BVAs) and quarterly business reviews (QBRs) that inform, educate and empower UA managers, CMOs and other decision makers to position their brands for success.

In this series, members of our Client Analytics team will explore marketing intelligence insights available to our customers from the tools Kochava provides.

Go for unique traffic

Here’s food for thought—how many of a partner’s installs are unique vs. influenced? In essence, does a media partner have unique reach, or are they stepping over the toes of another partner and bidding against each other for the same placements? With multi-touch attribution, Kochava can show the path a user took across multiple impressions and clicks to finally reach the point of conversion.

Not only can Kochava show you influencers that overlap across partners, but we can also give insight into self-influencers, or partners who repeatedly hit the same user again and again. Too many self-influencers from the same partner or DSP may suggest poor frequency capping, translating to a bad user experience. When two partners have significant influencer overlap, offering minimal uniqueness in reach, the marketer should consider trimming one of those partners. Ideally, you want unique traffic, with partners delivering valuable users that no other partner could have delivered.

Improve quality

Further insights can be gained by adding a qualitative layer that assesses performance by downstream completion of key performance indicators (KPIs). Kochava offers flexibility to customize and refine the analysis against vertical-specific KPIs, such as: free trial starts for a video streaming app, first, second and/or third order placements for a QSR app, and level completes or gameplay for gaming. Whether it’s a single KPI action or a combination/sequence of multiple KPIs, the time window within which these activities must be completed can also be refined.

Combining the influencer layer with the qualitative layer can offer a unique intersection of insights. For instance, compare the quality of the unique vs. influenced installs. Here, “quality” would mean installs with the completion of KPIs downstream within the optimal time frame. Ideally, you want to see higher quality for your unique traffic than your heavily-influenced traffic. That being said, in certain cases, trends may show that the combination of two or more partners influencing a user consistently results in higher downstream engagement with KPIs. This may suggest that the confluence of these media partners and/or the combination of marketing channels they traverse delivers a winning combo for engaging quality users.

Also, compare the percentage of unique traffic for all attributed media partners to the average unique traffic for the app. Make note of media partners with a below-average percentage of unique installs and look deeper into these media partners. Kochava can even decomp performance and quality at a much more granular level, such as by site or creative ID.

Unattributed (a.k.a. organic) traffic also offers a helpful quality baseline. Organic users are those who seek out and install an app on their own, without clicking on any ads. They often index higher on downstream performance and engagement with the app. Measuring paid media partners against organic quality trends allows you to see those under- or over-indexing on quality. Consider trimming partners that significantly underperform. At the same time, be watchful of partners that consistently parallel organic trend lines, as this may be a proxy for clever organic sniping tactics. Implementation of fraud tools to prevent click flooding and click injection will help mitigate organic sniping.

Use case

A gaming company ran ads with 10 media partners during April 2019, and they want to understand the uniqueness and quality of the installs driven by each partner to determine if there are opportunities to cut/expand marketing budgets next year. “Purchase” is their key KPI and they typically see purchases happen within seven days of the install so they want to see what percentage of installers had a purchase within seven days of the install.

The Uniqueness & Quality By Media Partner chart below shows the results of the analysis that the gaming company completed. After running this analysis they compare which media partners had unique and quality installs and determine that five of the 10 media partners they are running media with have lower than the overall average of 69% unique and also have unique quality that is lower than the overall quality.

The client decides to dive into the five media partners (media partners C, F, G, H, and J from the below chart) that had a very small amount of unique installs and those unique installs had lower quality than overall quality as a first step. After looking deeper at the traffic these media partners provided and the cost associated, the gaming company decides to cut back dollars put toward these media partners and invest deeper into higher quality unique sources. By cutting back dollars put toward these media partners, they can become more efficient with how they spend their marketing dollars to drive more high-quality, unique installs.

Here is an example of the results used to make their media partner cuts:

The takeaway

We typically recommend that marketers focus on the quality of a media partner’s unique traffic—all else being equal—those are the installs that the marketer would not have received without the partner in question.

In the table above, we see that media partner F was only 25% unique—meaning three-fourths of their installs would have attributed to other partners if partner F wasn’t in the mix. And, the influenced installs were of higher quality than what the partner uniquely touched. Overall, partner F is not contributing to better installs.

There are a number of ways to optimize your ad spend, and thoroughly evaluating your media partners is an important part. Contact Kochava to learn how you can leverage our turnkey partner analysis methods to see which partners are delivering unique, high-quality users.

On the lookout for your next media partner? Download the latest Kochava Traffic Index to see the top 20 ranked partners for Q1 2019.

Ready to start looking into your media partner mix? Contact your client success manager or support@kochava.com. With Kochava, you have a support team at the ready to meet your needs.

Not a Kochava customer? Contact Us Today.

Katie Darren – Client Insights Analyst

Kochava

The post Avoid an Overcrowded Media Mix appeared first on Kochava.

New Ways to Grow Your Audience

Katie Darren — Tue, 16 Apr 2019 21:44:51 +0000

Today’s marketer is data-driven. More than simply connecting our clients with their data, Kochava seeks to empower strategic decisions through marketing intelligence. Our growing Client Analytics team is comprised of data analysts who work hand-in-hand with clients, delivering face-to-face business value assessments (BVAs) and quarterly business reviews (QBRs) that inform, educate and empower UA managers, CMOs and other decision makers to position their brands for success.

In this series, members of our Client Analytics team will explore marketing intelligence insights available to our customers from the tools Kochava provides.

Targeting new users effectively is every marketer’s daily challenge. User acquisition or growth managers are tasked with reaching new high-quality users and that job doesn’t end when users install the app. Although people are spending more time on mobile devices, because of its overstimulated environment, retention drastically decreases with time, regardless of the vertical.

Without knowing, you may be mistargeting with irrelevant messaging or expending valuable ad spend on a less optimal segment. To reach high-quality users, revisiting your audience data and considering different ways to analyze it will shed light on better ways to target, and assessing app engagement is a great place to start.

How can you target more effectively? The One-and-Done analysis of user engagement

How a marketer determines engagement differs widely and is based on a variety of factors and nuances, such as the app vertical, how the app monetizes, and other variables. To help distinguish the different types of users in an audience, the Kochava Client Analytics team has a simple, customizable query called the One-and-Done analysis.

What is the One-and-Done analysis?

The One-and-Done analysis is a versatile SQL script that helps measure the quality of users where quality is the percentage of users who complete an event. The analysis can help you segment users under three classifications based on actions performed during a specified time frame:

The Non-Performer
- User installs the app and then disappears without completing a KPI

The One-and-Done
- User installs the app and performs one post-install action but never returns
The Performer
- User installs the app and performs consistently through regular engagement with KPIs

You can determine which KPIs to evaluate, the specific time frame of installs, and when the post-install events occurred. Then, use the Query tool to write SQL scripts to analyze, cohort, group, slice, dice, and pivot your data in unique, customized ways. Or, the Kochava team can help you customize the query for your specific brand’s needs.

In addition, measuring ad partners by how many users are classified as “one-and-done” is beneficial in comparing user quality by media partner. Partners that heavily over-index in one-and-done users can be trimmed or eliminated, whereas partners with the lowest percentages can be prioritized.

The potency of this analysis can be further increased by making sure the app is measuring a healthy mix of KPIs throughout the user funnel. See Post Install Event Examples for a list of recommended events to track by app vertical.

Below is an example of how you can visualize the data from the query to help distinguish the one-and-done vs. performer segments by media partner, providing insight into which partners drove quality user engagement.

Sample use case

A financial service provider specializing in money transfers has a goal to increase this conversion event and are exploring ways to grow their user base. In seeking assistance from the Client Analytics team at Kochava, they learned that a percentage of their converted users were “one-and-done.” Additionally, they learned which media partners repeatedly delivered this user type. They can now apply their learnings toward their next campaign by removing certain media partners and creating a lookalike campaign of their newly segmented performing users.

How can you apply the One-and-Done Analysis results?

There are several ways in which you can apply your learnings from the One-and-Done analysis to grow your audience:

Audience Targeting: Use your performers as a seed for a lookalike model via the Kochava Collective. Activate a campaign with the lookalike audience to acquire more users with attributes that commonly define performers, getting stronger ROI on your acquisition efforts.

Push Engagement: If your goal is to retain the one-and done user, load this audience segment into your push provider platform for a push, in-app message or SMS campaign to reengage them with a contextually relevant offer.

Compare Audience Insights: Marketers can load performer and non-performer and/or one-and-done segments into Kochava Collective’s Audience Insights engine to output demographic, behavioral, and a wealth of other data insights to inform optimal audience targeting and creative decisions.

Marketers can also export a list of device IDs by segment and activate them in either the Kochava Collective or with a media partner.

Grow your audience with the One-and-Done analysis

While you may already be segmenting your audience, the One-and-Done analysis brings highly customizable and flexible turnkey segmenting. If you haven’t identified and segmented one-and-done users, you are probably mistakenly considering them active performers.

If you are looking for greater insight into the quality of installs, the One-and-Done analysis may be a solution to explore. The output is easy to understand and can be automated on an ongoing basis to provide actionable audience segments.

Interested in refined and customizable user segmentation? Contact your client success manager or support@kochava.com.

Not a Kochava customer? Contact Us Today.

Katie Darren – Client Insights Analyst

Kochava

The post New Ways to Grow Your Audience appeared first on Kochava.

Beating the User Attrition Curve with Predictive Behavior Modeling

Katie Darren — Wed, 03 Apr 2019 21:32:24 +0000

Today’s marketer is data-driven. More than simply connecting our clients with their data, Kochava seeks to empower strategic decisions through marketing intelligence. Our growing Client Analytics team is comprised of data analysts who work hand-in-hand with clients, delivering face-to-face business value assessments (BVAs) and quarterly business reviews (QBRs) that inform, educate and empower UA managers, CMOs and other decision makers to position their brands for success.

In this series, members of our Client Analytics team will explore marketing intelligence insights available to our customers from the tools Kochava provides.

Nearly 70% of mobile users churn within 90 days. With this in mind, how do you keep the users you find? Predictive behavior modeling was developed by Kochava data science and engineering teams to help clients predict the churn of a user before it happens. This data empowers marketers to improve retention rates and navigate strategy toward better return on investment (ROI).

How predictive behavior modeling works

After a new install, our machine learning algorithms go to work using a form of decision tree modeling to analyze recency, frequency, trend metrics, and other data variables during the first 7 days of a user’s interactions with the app.

On day 8, the user is assigned a churn score. “Churn” in this case means how likely is the device to not have activity in the app between day 8 and day 38 after install.

A user with a score of “Low” indicates that it’s likely they will have further engagement with the app between day 8 and day 38. A user with a score of “High” indicates that it’s likely they will churn, or have no further activity in the app between day 8 and day 38.

Determining prediction accuracy

Predictions are only as good as their ability to accurately forecast. Assessing the accuracy of the predicted churn scores from the machine-learning model required analysis of the downstream activity for each churn group to see how closely the predictions aligned with the outcome.

Through extensive examination of multiple data sets and applications across various verticals, a common trend line emerged. With overwhelming consistency, users adorned with “Low” churn likelihood scores displayed the highest percentage of session and in-app event activity between day 8 and day 38 post install. Users scored at “Medium Low,” “Medium High,” and “High” exhibited an exponential decline in engagement over that period. With prediction accuracy confirmed and aligned with expectations, these churn scores provide valuable, actionable intel.

Applying predictive behavior modeling

Providing advanced insight into a user’s likelihood for attrition allows marketers to strategically intercept that user with targeted reengagement efforts. Using analytics, marketers can segment audiences according to churn-likelihood scores and syndicate “Medium High” and/or “High” likelihood segments to reengagement partners like Kochava for focused targeting campaigns to drive retention.

Marketers can build dynamic audiences around churn scores and set triggered push campaigns with contextually relevant, dynamic content to promote user retention and growth.

User audiences with a “Low” churn likelihood can be valuable seed audiences from which to build lookalike models to attract quality users with similar characteristics that promote user longevity and loyalty.

Predicted churn data informs insights into media partner quality. Brands are leveraging audience breakdown by partner based on churn score distributions. They can deprioritize partners that over-index in users with high likelihood for churn, and focus on partners who consistently deliver users with low churn likelihood and a higher ROI.

Maintain user retention

Predicted behavior modeling offers turnkey marketing intelligence with meaningful actionability. Reengage users most likely to churn with custom messaging via push notifications. Apply audience segments with a low likelihood for churn to create lookalike audiences of high-value users. Or, compare media partners and identify which ones deliver quality users least likely to churn.

To learn more about Kochava Predictive Behavior Modeling and how it can help your marketing efforts, contact your client success manager or support@kochava.com.

Not a Kochava customer? Contact Us today.

Katie Darren – Client Insights Analyst

Kochava

The post Beating the User Attrition Curve with Predictive Behavior Modeling appeared first on Kochava.

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