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Main article: Analytics

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Topics from 1 to 10 | in all: 327

Google closes $2.6B Looker acquisition

19:35 | 13 February

When Google announced that it was acquiring data analytics startup Looker for $2.6 billion last June, it was a big deal on a couple of levels. It was a lot of money and it represented the first large deal under the leadership of Thomas Kurian. Today, the company announced that deal has officially closed and Looker is part of the Google Cloud Platform.

While Kurian was happy to announce that Looker was officially part of the Google family, he made it clear in a blog post that the analytics arm would continue to support multiple cloud vendors beyond Google.

“Google Cloud and Looker share a common philosophy around delivering open solutions and supporting customers wherever they are—be it on Google Cloud, in other public clouds, or on premises. As more organizations adopt a multi-cloud strategy, Looker customers and partners can expect continued support of all cloud data management systems like Amazon Redshift, Azure SQL, Snowflake, Oracle, Microsoft SQL Server and Teradata,” Kurian wrote.

As is typical in a deal like this, Looker CEO Frank Bien sees the much larger Google giving his company the resources to grow much faster than it could have on its own. “Joining Google Cloud provides us better reach, strengthens our resources, and brings together some of the best minds in both analytics and cloud infrastructure to build an exciting path forward for our customers and partners. The mission that we undertook seven years ago as Looker takes a significant step forward beginning today,” Bien wrote in his post.

At the time of the deal in June, the company shared a slide, which showed where Looker fits in what they call their “Smart Analytics Platform,” which provides ways to process, understand, analyze and visualize data. Looker fills in a spot in the visualization stack while continuing to support other clouds.

Slide: Google

Looker was founded in 2011 and raised over $280 million, according to Crunchbase. Investors included Redpoint, Meritech Capital Partners, First Round Capital, Kleiner Perkins, CapitalG and PremjiInvest. The last deal before the acquisition was a $103 million Series E investment on a $1.6 billion valuation in December 2018.

 


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Placer.ai, a location data analytics startup, raises $12 million Series A

16:00 | 22 January

Placer.ai, a startup that analyzes location and foot traffic analytics for retailers and other businesses, announced today that it has closed a $12 million Series A. The round was led by JBV Capital, with participation from investors including Aleph, Reciprocal Ventures and OCA Ventures.

The funding will be used on research and development of new features and to expand Placer.ai’s operation in the United States.

Launched in 2016, Placer.ai’s SaaS platform gives its clients to real-time data that helps them make decisions like where to rent or buy properties, when to hold sales and promotions and how to manage assets.

Placer.ai analyzes foot traffic and also creates consumer profiles to help clients make marketing and ad spending decisions. It does this by collecting geolocation and proximity data from devices that are enabled to share that information. Placer.ai’s co-founder and CEO Noam Ben-Zvi says the company protects privacy and follows regulation by displaying aggregated, anonymous data and does not collect personally identifiable data. It also does not sell advertising or raw data.

The company currently serves clients in the retail (including large shopping centers), commercial real estate and hospitality verticals, including JLL, Regency, SRS, Brixmor, Verizon* and Caesars Entertainment.

“Up until now, we’ve been heavily focused on the commercial real estate sector, but this has very organically led us into retail, hospitality, municipalities and even [consumer packaged goods],” Ben-Zvi told TechCrunch in an email. “This presents us with a massive market, so we’re just focused on building out the types of features that will directly address the different needs of our core audience.”

He adds that lack of data has hurt retail businesses with major offline operations, but that “by effectively addressing this gap, we’re helpiong drive more sustainable growth or larger players or minimizing the risk for smaller companies to drive expansion plans that are strategically aggressive.”

Others startups in the same space include Dor, Aislelabs, RetailNext, ShopperTrak and Density. Ben-Zvi says Placer. ai wants to differentiate by providing more types of real-time data analysis.

While there are a lot of companies touching the location analytics space, we’re in a unique situation as the only company providing these deep and actionable insights for any location in the country in a real-time platform with a wide array of functionality,” he said.

*Disclosure: Verizon Media is the parent company of TechCrunch.

 


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Don’t be a selfless startup

01:21 | 15 January

One of the enduring truths of big companies is that they aren’t innovative. They are “innovative” in the marketing sense, but fail to ever execute on new ideas, particularly when those ideas cannibalize existing products and revenues.

So it often takes a real competitor to force these incumbent, legacy businesses to evolve in any meaningful way. Usually that change leads to disruption, in the classic way that Clayton Christensen describes in The Innovator’s Dilemma. An upstart company creates a new technology or business model that is better for an under-served segment of a market, and as that company improves, it competes directly with the incumbent and eventually wins over its market with a vastly superior product.

Unfortunately, real life isn’t so easy, as WeWork and MoviePass have shown us over the past few years.

In both cases, there were incumbents. In movie theaters, you had AMC and the like, who built a business model around ticket sales (shared with movie studios) and food/beverage concessions that targeted occasional customers at a high price point. Meanwhile, in commercial real estate, you had large landowners and family holders who demanded extremely long rent terms at high prices, often with personal financial guarantees from the CEO of the tenant firm.

 


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When and how to build out your data science team

19:56 | 13 December

Ganes Kesari Contributor
Ganes Kesari is a co-founder and head of analytics at Gramener. He helps transform organizations through advisory in building data science teams and adopting insights as data stories.

Increasingly, startups across the spectrum are looking to artificial intelligence (AI) to help them solve business problems and drive efficiency. The numerous benefits of building AI capability in  your startup shouldn’t come as a surprise to anyone — in fact, the advantages for business are so far-reaching that PwC predicts that AI will add $15.7 trillion to the global economy by 2030.

Contrary to popular belief, successfully implementing AI to drive impactful decisions requires a diverse team with expertise in several skill sets. Launching your AI journey is no simple feat — you need to ask probing questions to ensure that the relevant data science projects are embarked upon at the right time. Plus, you need to make sure that you build out an effective team that can turn data into decisions.

When should businesses take the AI leap?

 


0

Google makes moving data to its cloud easier

21:33 | 12 December

Google Cloud today announced Transfer Service, a new service for enterprises that want to move their data from on-premise systems to the count. This new managed service is meant for large-scale transfers on the scale of billions of files and petabytes of data. It complements similar services from Google that allow you to ship data to its data centers via a hardware appliance and FedEx or to automate data transfers from SaaS applications to Google’s BigQuery service.

Transfer Service handles all of the hard work of validating your data’s integrity as it moves to the cloud. The agent automatically handles failures and use as much available bandwidth as it can to reduce transfer times.

To do this, all you have to do is install an agent on your on-premises servers, select the directories you want to copy and let the service do its job. You can then monitor and manage your transfer jobs from the Google Cloud console.

The obvious use case for this is archiving and disaster recovery. But Google is also targeting this at companies that are looking to lift and shift workloads (and their attached data), as well as analytics and machine learning use cases.

As with most of Google Cloud’s recent product launches, the focus here is squarely on enterprise customers. Google wants to make it easier for them to move their workloads to its cloud and for most workloads, that also involves moving lots of data as well.

 

 


0

U.S. online shoppers already spent $50B in November, holiday season on track for $143.7B

18:43 | 27 November

Facing a shorter holiday shopping season this year, U.S. retailers started rolling out their Black Friday deals earlier than usual. That move has paid off, according to new e-commerce data shared by Adobe Analytics this morning, which found that U.S. consumers have already spent $50.1 billion online between November 1 and November 26, 2019 — which represents a comparable increase of 15.8 percent year-over-year.

This year, Thanksgiving arrived on November 28, a full week later than it did in 2018 when it came on November 22. That left retailers with 6 fewer days to drive post-Thanksgiving Day sales — a situation it hadn’t been in since 2013, when the shorter time frame led to serious delivery struggles. To salvage the lost shopping days (and to not again find themselves in a similar situation as 2013), retailers simply rolled out their deals a week early.

For example, Amazon kicked off a Black Friday deals week on November 22. Walmart introduced early savings through “Buy Now” deals on Walmart.com, in addition to a pre-Black Friday event that started on Nov. 22. Target integrated Shipt’s same-day shopping service into its app and ran a preview sale, weekend deals, and today, Nov. 27, an early access sale. Other retailers followed suit, as well.

But consumers weren’t even waiting for these Black Friday preview deals to start shopping. According to Adobe Analytics, which tracks online transactions for 80 of the top 100 U.S. retailers, all 26 days in November so far have surpassed $1 billion in online sales. Seven days even passed $2 billion in sales, which made 2019 the first year to see multiple $2 billion days this early in the shopping season.

And as of this morning, $240 million has already been spent online, representing 19.3% growth year-over-year, and putting the day on track to hit $2.9 billion.

 

Based on this data, Adobe believes its earlier forecast of $143.7 billion spent during the full holiday shopping season (Nov.-Dec.) remains accurate. That estimate represents a 14.1% rise from a year ago, according to Adobe. In addition, the three biggest shopping days — Thanksgiving, Black Friday, and Cyber Monday — will also see increases, it says.

Thanksgiving Day sales are forecast to jump 19.7% year-over-year to $4.4 billion; Black Friday is expected to grow by 20.5% to reach $7.5 billion; and Cyber Monday sales are expected to top the charts at $9.4 billion, an increase of 19.1% year-over-year — a new record.

The firm also sees a surge in mobile shopping this year, with 34.3% of all e-commerce sales being made via a smartphone, up 24.2% year-over-year. App Annie’s mobile shopping forecast had also predicted a record numbers of mobile shoppers, with a 25% year-over-year increase in time spent mobile shopping during the weeks of Black Friday and Cyber Monday. The firm said shoppers will spend 2.2 billion hours globally across shopping apps this holiday season.

Other notable trends include a rise in “buy online, pickup in-store” shopping — 61% will take advantage of this, leading to 27% more in sales over last year. Plus email promotions this season have led to 16.5% of all online revenue, up 10% year-over-year. Paid search accounted for 23.7% of sales, while social media led to just 2.8%.

In terms of products, shoppers are buying Apple AirPods, Apple Laptops, Samsung and LG TV’s, Frozen 2 toys, L.O.L Surprise Dolls, NERF toys, Pikmi Pops, Fortnite toys, and games like Pokemon Sword/Shield, Jedi Fallen Order, and Madden 20.

“With the shorter shopping season and retailers starting their promotions earlier, Adobe is seeing holiday discounts already well underway even before Thanksgiving Day,” said Jason Woosley, Vice President of Commerce Product & Platform at Adobe. “For televisions alone, shoppers are already seeing discounts twice as deep as expected with average savings yesterday of 17.5%. Those consumers who grab their smartphone to do some quick online shopping after dinner are likely to find offers that are even better than this time last year,” he added.

 

 


0

Signal AI taps $25M for public data-based market intelligence that spots trends and risks

09:29 | 22 October

Media monitoring — where news sources and other public information outlets are scanned regularly for mentions of specific organizations — is a well established service used by companies for market intelligence and to measure sentiment around their businesses. Today, London-based Signal AI, which has built a substantial operation in the area, has raised $25 million funding to expand to newer frontiers: applying AI to that public data to also spot themes, risks and opportunities to make better decisions; and continuing to take that business to new markets.

The Series C is being led by Redline Capital, with previous VCs MMC Ventures, GMG Ventures (an investment firm linked to the Guardian Media Group) and Hearst Ventures also participating. The startup, which has now raised around $53 million, is not disclosing its valuation but CEO and found David Benigson said that it is “significantly higher” than before (it last raised $16 million a year ago), after growing revenues at well over 100% each year for the last two.

The presence of not one but two media-linked investors in the round points to the startup’s roots: Signal AI had previously been called Signal Media and worked mainly around the task of media monitoring in the more traditional sense: tracking how companies were being mentioned in the press.

Benigson said that the reason for the rebrand was to “signal” to the world how the startup was widening its remit, both in terms of its sources of data and also in terms of its customers and how they now utilize Signal’s technology.

The challenge and opportunity that Signal AI is tackling is the fact that the world is awash in information, much of it unstructured and usually bombarding us from many angles, but tantalising all the same for hinting at the insights that it might hold if it could be looked at in a more comprehensive way.

“When we started six years ago, it was by aggregating news data and tapping the repository of global, traditional media,” Benigson said. “We have since broadened into social media, broadcast and radio, and regulatory information and started to apply more machine learning to structure that data.” The company also, in addition to selling services directly, now partners with third parties to build analytics around more targeted subjects such as a changing regulatory climate in a specific area, which in turn sold on by the third parties to other clients.

The company, for example, works with Deloitte’s tax division to monitor how tax codes are evolving and likely to move over time: the firm used to keep its own clients up to date verbally on these details, and now it sends alerts automatically with insights — a switch that Benigson said has saved the company $100 million a year in human and overhead costs.

Signal AI sits in a relatively new, not clearly defined area of business. It can be comparable with the likes of Meltwater, Cision (Gorkana) and even Dataminr when it comes to reading media in real time. But it also works a little like business intelligence or market analytics in its predictive analysis. The company refers to its specific area as “augmented intelligence”:

“There is a trend / emerging category that is far less crowded and defined than business intelligence or analytics,” Benigson said. “For me, it’s around taking those same values of BI and applying them to the world of data that sits outside the organization. There are very few companies that use augmented intelligence, although we are seeing management consultancy firms and others we potentially compete with convening around this space.”

It’s that open water that has attracted investors to the company.

“In this new digital era of news and content, having an adaptive platform to help the world’s leading organizations see around the corner is invaluable,” said Nicolas Giuli, Partner at Redline Capital, in a statement. “Signal AI’s team of data scientists and engineers have been at the forefront of the AI revolution and we are excited to take this journey with them as they continue to scale across the world.”

In this day and age, data is indeed very much a hot commodity, but I’d argue that it’s also a hot potato. By that, I’m referring to the rise of security breaches, people’s growing awareness of how their personal information is being used (and too often misused), and regulation that now draws lines on how data can be used, after organizations failed to draw those lines themselves. All of these have made concepts like data analytics and data mining, even around supposedly anonymised information, feel more nefarious and unclear in their target purposes and ends. That potentially spells out trouble ahead for companies that dabble in this space.

Benigson, for his part, was unequivocal on where Signal AI stands on any kind of anonymised or other potentially personal data:

“We purposefully avoid those data sets because we feel that the challenges are not being met,” he said. The exception, he noted, was in cases where a company uses its own internal data for its own purposes, but this does not feed into Signal’s AI engine, which focuses only on publicly-available third-party content. “We have no plans to incorporate that kind of data ourselves. We have an opportunity to do this in an ethical manner.”

 


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Alteryx acquires machine learning startup Feature Labs

17:32 | 4 October

Alteryx, a publicly traded analytics company, announced this morning that it has acquired Feature Labs, a machine learning startup that launched out of MIT in 2018. The company did not reveal the terms of the deal.

Co-founder and CEO Max Kanter told TechCrunch at the time of the launch, that company had been based on research at MIT that looked at how to automate the creation of machine learning algorithms. “Feature Labs is unique because we automate feature engineering, which is the process of using domain knowledge to extract new variables from raw data that make machine learning algorithms work,” Kantor told TechCrunch in 2018.

It is precisely this capability that appealed to Alteryx . “Feature Labs’ vision to help both data scientists and business analysts easily gain insight and understand the factors driving their business matches the Alteryx DNA,” Alteryx co-founder and CEO Dean Stoecker said in a statement. It’s worth noting that the company acquired another machine learning startup, Yhat, in 2017 and launched a new feature, Alteryx Promote, based on that technology later that year.

As for Feature Labs, writing in a blog post announcing the deal, Kantor and chief data scientist Alan Jacobson saw a partner that could help it grow faster while fitting the long-term goals for the company. Kantor and Jacobson also sought to reassure its users that the mission will continue. “We plan to use this [acquisition] to expand our AI and ML efforts in both the Open Source data science community, as well as for line of business analysts that desire code-free tools that can guide them through the complex process to successfully implement AI and ML techniques with their domain knowledge,” they wrote in the post.

Feature Labs offers open source libraries for data scientists that have been downloaded over 350,000 times, according to the company. The company was founded in 2018 in Cambridge, Massachusetts and has raised $3 million, according to Crunchbase data. It will remain in Cambridge and form a new Alteryx engineering hub in the city.

Alteryx went public in 2017 after raising over $160 million from VC firms like Iconiq Partners, Insight Venture Partners and Sapphire Ventures. This represents its fifth acquisition and second this year.


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Cracking the code on podcast advertising for customer acquisition

23:45 | 27 September

Krystina Rubino & Lindsay Piper Shaw Contributor
Krystina Rubino is a marketing executive who leads the offline growth marketing practice at Right Side Up. Lindsay Piper Shaw is an advertising strategist and growth marketer currently consulting on podcast and offline advertising at Right Side Up.

Of the various channels available to growth marketers, podcast is among the most misunderstood.

Brands like Dollar Shave Club, Squarespace, and ZipRecruiter have deployed podcast advertising for user acquisition for years, but it’s still a channel that flies under the radar. We have managed tens of millions of dollars in podcast ad spend for challenger brands and market leaders alike, and are eager to share some tricks of the trade.

If you want to test in a channel where early adopters are being rewarded with both attractive CAC and scale, here’s what you need to know:

  1. Podcast advertising is used very successfully as a direct-response channel with CAC on par with other consideration-stage activities. It is not just for awareness.
  2. Podcast reach is very good, reaching 51% of US audiences aged 12+ monthly.
  3. Ads read by hosts outperform canned “programmatic” ads.
  4. Tracking is harder than most digital channels and the cost to test the channel is higher than most digital channels.

Dive deeper on podcast ads and other growth marketing tips with Extra Crunch’s ongoing coverage of growth marketing, where Right Side Up was recently featured as a Verified Expert Growth Marketer. 

Who listens, who advertises, and why bother?

Podcast listeners are a sought after group – the audience trends towards educated, early adopters with a high household income. You can find this profile elsewhere, but what makes podcasts unique is that they are choosing to consume that particular content time and time again. The host becomes a trusted voice to deliver them not only interesting stories and banter, but information on companies as well.

Often podcast advertisers are newcomers or start-ups, and the podcast ad might be the first time the listener has heard about that company. Having the first touch with consumers be from a thorough, personal, and often funny host-read interaction is incredibly valuable and helps brands jump over the credibility hurdle. Compare that to an impersonal banner ad, and I’d choose a podcast ad every time. image2 1

Even though the term ‘podcast’ was coined in 2004, advertising in the medium has exploded in the last ~5 years. The IAB has been tracking podcast ad revenue since 2015, when the entire medium generated #105.7 million in ad sales. It recently released its third study of podcast ad revenue, which estimated the US market at $479 million in 2018, with growth accelerating to a projected  $1 billion+ by 2021.

image1 5

Andreesen Horowitz did a great investor profile on the space earlier this year, with a helpful rundown of the holistic ecosystem, from hosting mechanisms and platforms to the pace of podcast monetization.

Historically, the medium has been dominated by a mix of comedians doing their own thing, radio entities simulcasting sports shows, and otherwise popular shows that had a devoted niche following relative to other mediums. Most advertisers bought podcast ads as an extension of their other audio acquisition campaigns.

Podcasts go mainstream

Then Serial came along, in 2014, exploding into popularity and pop culture. They ran a MailChimp ad that had someone mispronouncing the name of the company as “MailKimp”, which was a funny inside joke for those in the know. Nina Cwik and David Raphael, co-founders of Public Media Marketing, explain the initial conversation around this now iconic spot.

“While discussing a launch sponsorship with sponsors there wasn’t a huge amount of interest in taking a risk on a new show even with the amazing This American Life provenance. MailChimp was committed to supporting Serial. The talented production team at Serial and This American Life created MailKimp and the sponsor was rewarded for believing in the show.”

Not only were they rewarded by being a launch sponsor of one of the most successful podcasts in history, but once Serial and the medium itself expanded, a loving impersonation of Serial host Sarah Koenig and the MailKimp joke eventually made its way into a Saturday Night Live skit. Serial also appealed to a female audience, helping to bring new listeners into the channel, and podcasters and advertisers followed.

Over the past 5 years, the space has diversified. We now see so many different shows with all flavors of true crime, news and politics takes that you don’t hear in the broader media picture, women talking to other women about literally everything, comedy and pop culture pods as diverse as Bodega Boys, Who? Weekly, and RuPaul: What’s the Tee with Michelle Visage, and a podcast to go with every reality and television show you can think of. There are too many shows to talk about; there are over 750,000 shows indexed by iTunes.

How to engage for growth advertising

So how do companies start testing in podcasts? And how do they do so successfully?

Start with a strong (but doable budget) and take your time

We advise companies to start with a test spend that you consider meaningful in the context of your other customer acquisition efforts. Initial tests in the channel that are properly diversified typically vary from $50,000 to $150,000 in media cost. If the idea of a testing budget in the high five figures makes you gasp, don’t rush it. If you under-invest, you run the risk of a false negative, i.e. you didn’t spend enough to validate performance, or a false positive; when you buy tiny shows, one or two sales may pay back. If you make media decisions at scale based on that data, you may find yourself in deep water. If the risk of testing a new channel and having a dip in your CAC is too great, we recommend you exhaust other channels, like Facebook, before jumping into the podcast space.

Podcast offers advertisers a low barrier to entry. Creative production is limited to producing copy points for hosts to use as they record their ad reads. However, it is quite manual relative to digital channels, and can take weeks to put into place. Most purchasing is done through a show’s sales representation or network, via calls and emails, and set in advance (sometimes way in advance depending on inventory levels). It entails RFPing multiple network partners, doing research and outreach to independent shows, gathering rates and evaluating content, and finally making decisions based on budget and inventory availability. We often describe this as the media puzzle – making sure that the ideal shows, with favorable pricing are available when you want them to be. This can take time and some back and forth with your network rep to set in stone, so give yourself room to plan ahead.

What’s the media landscape look like and how do you pick shows? 

GettyImages 532749101

Image via Getty Images / venimo

We buy with a lot of direct shows, sales representation firms, and ad networks. We’re starting to see the beginnings of programmatic and exchange-based inventory become available, but it’s largely impression-based media, which isn’t yet a proven tactic that direct response-oriented advertisers can consistently use for customer acquisition. There are some managed service-like buying partners in the space, that work to varying degrees of efficiency for customer acquisition.

When it comes to choosing what types of shows to partner with, beyond budget and availability, it’s important to remember the obvious choice may not be the best one.

One of the most consistent, and pleasant, surprises in podcast advertising is how well shows that are seemingly unrelated to a product work well for customer acquisition. We’ve worked on products that had a primary target demographic of suburban moms, but guess what? Gamers want to stay at home and order snacks and food delivery, too; they have disposable income and are harder to reach via traditional channels.

If you’re advertising a product targeted to parents, you shouldn’t just test into parenting shows, you should also consider testing into shows with hosts who are parents, but have content not at all or tangentially related to parenting, like Your Mom’s House, with Tom Segura and Christina Pazsitzky. Sure, it’s a comedy podcast, and it’s NSFW (and hilarious). They’re also human parents who they do amazing reads, and their fans are legion.

Ryan Iyengar, CMO of HealthIQ, notes that “hosts with wildly different backgrounds were able to find a through-line to connect ad reads with their audiences, regardless of product line.” Of course, contextual advertising is worth consideration, and there are sometimes unique opportunities, but most successful shows aren’t a bullseye for content.

We’ve also seen the inverse, on contextual fit; food products can either do amazing or not well at all on food-related podcasts. If you have a food product with mass appeal, but one that (for example) many home cooks may already be familiar with, you may be better off doing just about any other popular genre of shows besides food.

Plus, these hosts are pros; they’ve been doing ad reads for everything from mattresses to meal kits for years. They know how to talk about your product in an engaging way.

Doug Hoggatt, the VP of Marketing at Betabrand, agrees, mentioning he would also coach new advertisers to “take the time to test across genres and hosts, you’ll be surprised at the results.” Iyengar is also the former VP of Marketing at ZipRecruiter; if you’ve ever heard a podcast, you may have heard the company advertised once or twice. He also notes, “[regardless of] content of the show, audiences can be interested in all sorts of topics, and are still potential customers. Yes, even hiring managers listen to comedy podcasts!”

Many business-to-business (B2B) advertisers do well in the channel, in part due to higher allowable CAC and high lifetime value (LTV). And the same point about show selection holds true for those audiences, as well. Visnick noted, “[HoneyBook] originally focused on testing industry-specific podcasts as those seemed to be the most natural way to target our prospective customers. We discovered that by diversifying our podcast mix into non-industry content we could still reach our target audience while also growing our reach and overall program performance.”

If we hear something that we think can help us at work, we’re amenable to that message, especially when it comes from our favorite host. Having an open mind to testing has helped so many advertisers unlock additional shows, and possible customers. You can take those insights back to other channels, too, and begin to integrate your campaigns and establish cross-channel frequency.

Pricing in the channel is unstable, and demand-based because inventory is finite; effective CPMs for host read, embedded mid-roll advertisements — by far, the most consistently performing ad unit for customer acquisition in the space — vary from $10 to $100. Yes, really.

Worrying too much about CPMs could mean that you’re leaving behind some of the best inventory in the space. So while it could make sense to cut higher CPM placements from a media plan, you want to be cautious. You could inadvertently cut out potential volume drivers or otherwise highly effective placements.

Allow for the host’s personality to shine through

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Image via Getty Images / TwilightShow

The listener is there for the hosts. They relate to them, laugh with them, or laugh at them. They come to expect a performance from them, and often that performance bleeds into the ad reads. Whether it’s a semi-NSFW jingle about MeUndies from Bill Burr, or Joe Rogan recommending his mind-blowing NatureBox snack combination, or Levar Burton delivering an oh-so soothing Calm read.

Alan Abdine, Senior Vice President of Business Development for Rooster Teeth, a network with geeky, gamer shows with a hint of irreverence, said “the best ads are the ads that are organic, natural, and originate from the voice of the show talent. When brands allow our hosts to be themselves, there are more opportunities for entertaining side stories and commentary related to the brand.”

He continues to say his “belief is that if an advertiser is willing to spend money to reach out audience, then let us be the experts on that audience and let us use our own voice to share their message and talking points!  They will always get better results in that scenario.”

There is a certain special trust that goes into podcast ads. And to allow hosts to be themselves while also being a positive brand advocate often mean striking a balance between scripting and giving space. The most commonly purchased ad unit for customer acquisition advertisers is a host-read, embedded, mid-roll advertisement, typically :60 in length, but many hosts go over.

Overly scripting the copy can lead to an ad sounding inauthentic and infringe on their creativity. Kate Spencer, the co-host of Forever 35, notes that “often there are a lot of required talking points to hit in a short amount of time. We’re always happy to oblige, but I think it takes away from the organic and conversational nature of the ad, which is what makes podcast advertising especially unique. ”

On the flip side, not scripting enough could lead to a disjointed read where the host is trying to piece value props together on the fly. Nick Freeman, Chief Revenue Officer at Cadence13, explains that “some hosts do like the perfectly written out :60 script, while others like bullets they can riff off of.” Because podcast campaign test across multiple shows and personalities, it’s best to find a starting point in your copy where hosts can be guided, but not stifled. Freeman says “that doesn’t necessarily mean trying to make jokes for comedy hosts, for example, so much as it’s giving the hosts who do well with it the freedom to ad-lib.”

And for those that want to get a little more creative, the space is primed for custom integrations. Recently DoorDash partnered with Rooster Teeth for an ad on a livestream in celebration of a new game their studios were releasing. Since there was a visual element, DoorDash and Rooster Teeth partnered on a creative spin to the ad.

Instead of the typical copy, food would be delivered to the group of hosts while recording. Grant Durando, Senior Marketing Consultant at Right Side Up, works with DoorDash on their podcast campaign and stewarded this unique partnership. “[Rooster Teeth] approached us with the opportunity to engage with the live stream in a deeper way than just a regular podcast ad. It was definitely an unorthodox integration, but exciting to be in front of the right audience for DoorDash, at scale, and in a meaningful, memorable way. Many conversations about chicken nuggets later (which I never thought would be part of my job), Rooster Teeth and Vicious Circle delivered a superb ad experience, [integrating] multiple brand mentions and actually making DoorDash a part of the content itself.”

Zack Boone, Senior Director of Sales at Rooster Teeth, added there is, “nothing better than having clients that understand how impactful utterly stupid things like this can be for a brand.” DoorDash “[offers] industry-leading selection to our customers,” said Micah Moreau, VP of Growth Marketing at DoorDash. “It was incredibly effective to bring the DoorDash experience to life with Rooster Teeth in a highly differentiated, yet relevant way.”

How do you measure response?

Ads almost always end in some sort of call to action, like use the show’s promo code to save money, or visit a URL to get a free trial of a product for listeners of the show. It’s a way for shows to get credit for their listeners taking some sort of action, usually a purchase, related to hearing the ad.

And it’s how advertisers can figure out if their ad investments are paying back, too. Along those lines, Hoggatt was happy to see “how direct response the channel could be. I was surprised at the lift in site visits and follow-on orders that correlate so closely to when our podcasts drop.” Consumers have been conditioned to listen for that call to action at the end of an advertisement so we can measure a direct response in the channel.

That isn’t to say podcast advertising should displace a highly effective channel like paid social or paid search in your paid marketing testing priorities. We often ask advertisers information about their overall CAC or CPA  from other paid marketing efforts like Facebook or Google advertising, and use that data to benchmark target CAC for podcast.

As a general rule of thumb, if you can’t make Facebook or Google work for customer acquisition at meaningful scale, think twice before you engage in testing podcasts at a scale meaningful to your business. But if you’re looking for demand generating channels, podcast is an excellent contender.

“The success we’ve seen from podcast advertising has proven that we can drive sales through paid media outside of “traditional” direct digital response campaigns,” said Visnick. “We’ve significantly grown our podcast budget every quarter since we started testing the channel and it’s now a core part of our overall acquisition strategy and an important part of our media mix.

Don’t under-account for breakage or indirect activity

GettyImages 1160364589

Image via Getty Images / Olivier Le Moal

Another challenge for advertisers that aren’t used to offline channels is managing indirect activity, also sometimes called breakage. It’s imperative to look at indirect activity to help triangulate response, as another way to get a false negative is to only look at direct response, i.e. direct redemptions of a promo code or sales from only users who visited the vanity URL.

A decent analog is like view-through conversions, but without the technology enablement. You can tell, via tracking, what actions site visitors have taken after exposure to ads on Facebook and Google, etc.

However, there isn’t a way for a consumer to tap or click on your podcast ad, so you don’t have a direct action correlated to ad download or exposure, nor can you track indirect activity (view-through) via pixels or other technology enablement. The aforementioned promo code/vanity URL combo is what generates that direct response.

To get around this breakage and triangulate a full response, advertisers commonly use a post-conversion attribution survey, colloquially referred to as a How Did You Hear About Us? or HDYHAU survey. This allows for a crude, but effective, translation of the impact that podcasts had on that user’s activity.

It helps you determine how much of the activity you’re capturing in paid search, for example, may have actually been driven by podcasts, streaming audio, or television. It’s self-reported data from users, sure, and it can feel a little shaky when you’re used to more precise digital measurement, but it’s how virtually every scaled advertiser in the channel has discovered a path to scale.

It also helps you determine benchmarks before you get into other channels, and can provide a solid look at multi-touch attribution if the survey is designed with best practices, and served to enough of the population to achieve stability.

Why can’t we use measurement techniques from other mediums?

We already talked about why, even though podcasts are digital audio, we can’t track conversions digitally (we know, it’s a little crazy). Unlike television, where you can use spot-based attribution, or radio, where you can achieve consistent ad exposure and but according to average quarter-hour (AQH) ratings, there’s a delay in both download of an episode and media consumption.

For advertisers, that means performance comes in over time, and it takes a minute to build reach and frequency (R/F). You may see very little activity for the first week or two of a campaign, and then as R/F builds and crescendos, you’ll see conversion activity catch up. That’s when you can start to get a solid picture of return on ad spend (ROAS); you should have structured your tests so you have a good sense of performance by the third or fourth drop with a show.

Looking at results sooner is possible but largely inadvisable. “Give it time,” says Dan Visnick, CMO at HoneyBook, “It can take a few weeks to see the impact from a single podcast, and months to build a strong portfolio.”

One of the biggest mistakes new advertisers in the channel make is getting a false positive, by testing into tiny shows that back out because 2 people bought their product, and then quickly scaling in the same genre only to find out that the content doesn’t scale.

False negatives are also common, when advertisers get cold feet in the first few weeks of an integration, and cancel shows after one ad insertion in a single episode. The channel requires diligence in testing, and if you have other business challenges to navigate, using digital growth channels can help iron out your messaging, landing pages, etc. before you launch offline channels.

Although you may have honed your messaging in other channels, you should expect to be flexible when it comes to podcast creative.

Opportunities to expand to other audio acquisition opportunities

GettyImages 540366190

Image via Getty Images / Anastasiia_New

Positive signals in podcast campaigns can also indicate that other audio channels may be ripe for testing, which can help diversify your marketing mix and minimize the pressure on individuals channels. Hoggatt says his “success in podcast advertising proved that it is possible to invest in offline channels and find measurable success.”

SiriusXM and streaming platforms, whether pureplay like Pandora or Spotify, or aggregators like Westwood One and ESPN, are great next steps for advertisers who see the right signals in podcast. For SiriusXM, it’s a high household income audience that are used to paying for a subscription (any subscription model companies out there?), and streaming audiences are choosing to listen to their content, similarly to how podcast listeners choose their content. The podcast landscape is the perfect arena to play in to learn more about how your brand works in offline media and allows there to be a stepping stone into other mediums.

Be good stewards

We know that podcast advertising can have a powerful impact on the marketing mix for companies of all sizes. As more and more players get involved in the space, it benefits all involved, from advertisers, to networks, to marketers.

It’s rare to have an opportunity to participate in a nascent medium, and be good stewards of one of the last remaining mediums on earth with finite inventory and listeners who actually respond to ads. And along the way, we hope to change the way people think about traditional offline media channels, like how they can be held to high growth performance standards, and where they intersect with popular digital growth tactics like paid social.

You’ll have to get creative, but with some trust and patience, and adherence to best practices, advertisers can reap significant benefits and customer acquisition, at scale, from podcast advertising campaigns.

9 things growth marketers should do when getting started:

  • Create the team (and time!) needed to execute a campaign, whether in-house or via partnership with a subject matter expert like a consultancy or agency
  • Learn the language of podcast advertising, terms like download carry a lot of baggage and understanding them can impact your campaign’s performance
  • Budget your initial test(s) appropriately to avoid a false negative or positive result
  • Have an open mind on show selection; make sure you test across multiple genres and formats
  • Measure direct and indirect activity, to triangulate performance and business impact, and make optimizations and decisions on renewals
  • Support, don’t stifle, the personality of the show hosts
  • Get comfortable getting creative, and take time to onboard hosts
  • Keep an eye out for additional opportunities, not only in podcast, but in other audio channels as well
  • Be a good partner to shows, networks, and others in the space. It’s ours to nurture

 


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App Annie acquires analytics firm Libring, bringing ad tech-related insights to its platform

19:50 | 26 September

App Annie, a go-to source for mobile app market data and analytics, is expanding its platform with the acquisition of mobile analytics provider Libring. The deal will allow App Annie to present its mobile app market data side by side with advertising analytics data, in order to paint a more complete picture of an app’s performance and revenue.

Already, App Annie customers leverage its platform to track key metrics related to their app’s growth and usage, like downloads, active users, retention numbers, demographics, rankings, reviews, competitive analysis, and more. But the company said it heard from publishers and brands how it’s still difficult to analyze their user acquisition efforts, including their ad spend and related costs.

Screen Shot 2019 09 26 at 12.42.07 PMWith the addition of Libring, App Annie is integrating ad tech insights into its platform.

This includes the ability to combine the ad spend and monetization insights from over 325 data sources including Supply Side Platforms (SSPs), Demand Side Platforms (DSPs), app stores, and analytics platforms.

This data is then presented in a single dashboard so it’s easier to understand critical metrics — like the customer acquisition cost, the lifetime value, the return on ad spend, and the return on investment.

It’s ideal for larger organizations who have outgrown the spreadsheet, as it’s been sort of the App Annie of revenue aggregation, so to speak.

“The most successful companies find a way to capitalize on mobile, yet they have been struggling to maximize its value to their business,” explained App Annie CEO Ted Krantz, in a statement about the acquisition. “Today, this requires custom work to stitch together multiple point solutions, spreadsheets, business intelligence teams, agencies, and consultants. We are committed to solving this by applying data science and machine learning to automate these composite metrics for brands and publishers,” he said.

The deal comes at a time when mobile ad spend is continuing to grow rapidly — it’s expected to double to $375 billion globally by 2022, the company noted. It’s now a massive part of the overall app industry, at triple the amount of consumer spending on the app stores.

As a result of the deal, Libring’s 30-plus employees are joining App Annie.

In the near-term, Libring’s current customers will continue to use its product as they do today.

But App Annie tells us there’s only some overlap between the two companies’ respective customer bases. For now, App Annie will work with its customers who want to purchase the new analytics service and find out what sort of enhancements they are looking for in an analytics solution. Libring’s customers can also choose to buy App Annie’s analytics, if they choose.

Later, App Annie will migrate the Libring backend to the same infrastructure provider the rest of App Annie uses, and will then integrate the front-end so customers can log in and visualize the new analytics and other market data together. More information about how this will all work will be shared when those tools are closer to being available, which is still several months from now.

Going forward, App Annie says its data science team will also offer predictive and prescriptive insights based on the new data.

According to Libring’s website, its customers included SEGA, Slickdeals, Reddit, Jam City, Wooga, EA, Zynga, Next Games, Meet Me, GameInsight, Deviant Art, Webedia, Ubisoft, theChive, saambaa, badoo, textnow, and others.

App Annie declined to disclose the deal terms.

Related to the changes and expansion, App Annie also today introduced a new brand which features a gem logomark. The gem is meant to be a tribute to mobile gaming and the idea of “leveling up” while also a reflection of the value of actionable data, the company says.

AppAnnie Rebrand Logo Lockups DARKBLUE 1

The acquisition comes on the heels of several notable milestones for App Annie, including the launch of a product development testing ground, App Annie Labs; plus the addition of mobile web analytics in March — the same time when App Annie passed $100 million in annual recurring revenue.

The company is soliciting feedback about its plans for Libring and will post updates about the project on App Annie Labs, it says.

 


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