How does AI influence SEO?

• Identifying SEO Opportunities: AI simplifies the process of uncovering high-impact keywords and link-building opportunities. By analyzing large volumes of data, it identifies patterns in search behavior and backlink strategies that can drive qualified traffic to your site. These are some tools that support this process:
  • InLinks: Uses natural language processing to identify keywords and key phrases that can increase CTR (click-through rate) and conversions.
  • Frase.io: Ideal for keyword research and better understanding the search intent behind the most relevant terms for your niche.
  • Scalenut: Provides keyword research and key metrics such as search volumes and competition.

• Content Generation and Optimization: AI improves the structure of the page and optimizes content generation, creating content that responds to the user’s search intent.It can help structure articles, suggest headings, and even generate complete drafts. Tools include:
  • Surfer SEO: Tool specialized in content optimization through on-page signals.
  • Writesonic: Especially useful for creating long-form and structured content.
  • Jasper AI: Generates high-quality, SEO-optimized content quickly and can also create images with AI to improve the visual experience on the web.

• Optimization for Voice Search: Voice search is now commonplace, so it’s essential to adapt your content to this type of conversational search. You can use the following tools to optimize voice search.
  • Yext: Specializes in optimizing content for voice search and virtual assistants.
  • Frase.io: This tool can optimize content for conversational queries that are common in voice searches.

• Improved User Experience(UX): A well-structured website not only improves user retention, but is also favored by search engines. AI improves website structure and navigation, making it easier for users to find what they’re looking for. We can mention:
  • NitroPack: Specializing in improving web page loading speed, which improves user experience.
  • Jasper AI: generates recommendations on how to improve content to enhance the user experience.

• Website Audits and Duplicate Content Detection: AI audits are capable of detecting technical issues, such as duplicate content, that affect SEO rankings. These are some of the tools that help you making corrections or suggesting changes to improve the quality and relevance of the content.
  • DiiB: Offers detailed AI-based audits that detect duplicate content and other SEO issues, suggesting specific improvements.
  • AlliAI: Automates technical audits and real-time adjustments to efficiently optimize both content and code.

Another tool worth mentioning is ChatGPT, which help with: content optimization and voice search, as well as identifying SEO opportunities.

AI continues to expand its influence across the entire SEO landscape, and as these technologies advance, the possibilities will multiply. If you’d like help identifying the right tools or implementing AI in your SEO strategy, feel free to reach out. We’d be happy to support you!

The post Applications of AI in SEO appeared first on saltypistachio.

Here’s a breakdown of key areas where generative AI is making a significant difference in digital marketing:

• Data Analysis and Audience Segmentation: AI can analyze large amounts of data in real time. Furthermore, it can segment audiences into specific groups based on their interests, behaviors, and preferences.This allows marketers to segment audiences into highly specific groups based on behavioral data, interests, location, device usage, past purchases, and even in-app interactions.

• Content Personalization: AI enables content personalization based on user behavior and preferences. AI uses customer data to personalize messages, product suggestions, and content, boosting user experience and driving higher conversions. With AI, businesses can create tailored emails, recommendations, and visuals that resonate, enhancing engagement and increasing conversion rates.

• Chatbots: AI-powered chatbots go far beyond answering FAQs. In fact, they can provide assistance and answer customer questions in real time. As a result, this promotes a better customer experience by providing quick responses to their needs.

• Process Automation: AI can automate repetitive and routine tasks. This can automate sending emails, posting content on social media, and managing advertising campaigns. This frees up time and resources for marketers to focus on strategic and creative activities.

• Campaign Optmization: AI helps maximize ROI by analyzing campaign performance data and making real-time adjustments for better results. It allows marketers to fine-tune messages and offers based on audience behavior and engagement patterns instantly. With AI, advertising campaigns can become more adaptive, targeting the right users at the right time.

Some of the most notable benefits of AI in digital marketing include process automation, greater personalization, and enhanced decision-making. The key now is to leverage AI with clear intention, a well-defined strategy, and a strong commitment to ethical use. As this technology continues to evolve, marketers who adopt it thoughtfully position themselves to drive meaningful results and build lasting connections with their audiences.

The post How Generative AI is Transforming Digital Marketing appeared first on saltypistachio.

Let’s start with why it was understandable:

Years ago, the app stores (especially Apple’s App Store) did not provide much in the way of data analytics when it came to creative testing. The Google Play Console did allow for basic AB testing with Google Experiments, but there wasn’t (even today) any way to control of the source of traffic. In our view, this renders AB Tests invalid: If one of your variants matches that of an acquisition campaign accounting for most of your traffic, it might outperform other variants, but this doesn’t mean it was a better performing.

There was clearly a need for companies (especially in the gaming space) to optimise app store creatives. After all, a 5% improvement in conversions thanks to AB testing can equate to huge savings and increased organic downloads for high volume apps (and/or those with high CPIs). Enter companies like StoreMaven and SplitMetrics (now Splitmetrics Optimize):

It worked a bit like this: You could create a ‘pretend’ App Store Product page that had all the tracking that the App Store or Play Store didn’t provide: You could get click tracking, heatmaps, and even how many images were scrolled or for how long they spent viewing videos.

Clearly, having this information would be useful, but there were a couple of ‘catches’: As mentioned, the traffic was going to a ‘pretend’ Product Page, that once the user decided to install, they were sent to the real Product Page. Of course, that leads to some users being confused and abandoning their intention to install.

The other catch is that you needed to send traffic to those pages. For instance, you could send traffic from a Facebook Ads campaign (and this before counting on the cost of using the platform – especially considering their opaque pricing)

Clearly, both things combined meant there was a significant cost to learning. Still, for the right company this could be valuable. And so many gaming companies used these kinds of tools.

Move forward some years and while Google Play Console’s capabilities remain pretty much untouched, the App Store first added Creative Tests on Apple Search ads, to then improve the testing capabilities with last year’s introduction of the Custom Product Pages:

Apps can now create up to 35 Custom Product pages (read Landing Pages) and send traffic to these. Not only Tap Through Rates and Taps to Install can be asserted, but also the ultimate CPI. And if you use a tool like Appsflyer, you can even learn ARPUs or LTVs per user. All of this directly from within Apple’s Search Ads.

It’s no wonder the value of these tools like StoreMaven was decimated. We’re certain some companies could find an edge in using those marginal games, but we’d be hard pressed to recommend them ahead of the benefits of using Apple Search Ads directly.

Let’s move to the why it raised eyebrows:

And so, it wasn’t that surprising these companies ended up struggling, or that StoreMaven was acquired. Yet what we found surprising was the lack of buzz at Activision / Take2 being the acquirer.

It clearly makes sense for a company like Activision – counting with loads of games in the app stores to get expertise leading to improvements in the Stores. We’d guess some of their apps would have been some of their last loyal clients. But it must have been quite a reaction for CMOs of Activision / Take2 competitors who used StoreMaven: Surely their first thought must have been regarding experiments data carried during years.

Not only they spent traffic to acquire that traffic over years and paid their licenses for it, but Activision and their apps could now correlate their experiments with their decision making on creative iteration for years. AFter all, tools like MobileAction or AppTweak give visibility on historical changes made.

More recently, German agency Phiture acquired ASOGiraffe (a small competitor of StoreMaven), so a similar situation arises: If you used ASO Giraffe and your competitor uses Phiture, they’ll have indirect access to your data.

Had these been acquisitions at larger scales, we think it would have triggered concerns by competition regulators. And so, it makes us think that for any brand, using these kind of analytics raises a grave concern: What if your data ended up with your competitors?

The post When your app store data gets used by your competitors appeared first on saltypistachio.