How To

How Our Psychometric AI Audience Analysis Works

By | How To

Who is listening to your brand? Who is your customer? At the core, marketing is about understanding your consumers and tailoring your content to engage them on a deeper level. Budweiser knows its audience is largely blue-collar workers, so each Super Bowl, they tailor their commercials with imagery that makes their customers feel known: farms, factories, rural America.

Those commercials would be less effective if Budweiser didn’t know their audience. Audience analysis allows you to understand which content topics and formats are interesting to your audience. You can use these insights for long-term content strategy and to better and more efficiently organize your content and marketing teams.

Consumers want content tailored to them. Personalisation is the future. 83% of executives are increasing their investments in personalisation. But do you know how to optimise it? Less than 40% of companies are using basic targeting. Learning how to optimise AI and personalisation is key to growing your audience and reaching your consumers. And we can help demystify it.

Great audience analysis will allow you to start your marketing strategy by building an effective content marketing funnel based on your audience’s interests. Every single content piece you put out is aligned with your audience’s interests and therefore successful in moving users towards conversion. When you know your audience, you can craft tailored content at each stage of the journey.

Brands traditionally have relied upon analysis of what users like online and similar brands. However, technology has greatly advanced, and we have the opportunity to dive deeper into our consumers’ real personalities. Using Watson’s AI Personality Analysis and our platform, we can take your user base and then aggregate and anonymise that data (always gotta be GDPR compliant!). The result isn’t a guess at who your customers are, but a list of the most common personality traits and what motivates your audience. We’re able to use your audience’s writing style, tone, and likes to create an accurate personality model of your base.

Audience Analysis of Formula E

You can see that the average consumer for Formula E is an Entrepreneur.

Audience Personality

That info in and of itself can revolutionise your marketing funnel. But we didn’t stop there. We’ve taken those insights and coupled it with our AI-powered content optimiser. We can then analyse your content and make real-time recommendations based on who your audience is.

AI Recommendations

For example, in the report above, we can see that content that is tweaked to be more direct, put Formula E out there more, and be more supportive would better resonate and convert the core audience. As Formula E adjusts their content, the AI recommendations adjust as well. So, you can see the fruit of your labors and know what’s working.

So, how does it work exactly? We partnered with IBM Watson AI engineers for the past three years to develop this program. By detecting the profiles of everyone who interacts with your brand and competitors, Audience Analyser automatically captures all the publicly available social data from those mentioning and following your target audience across social media. It psychometrically analyses millions of posts to create personality maps around 50 core traits based around OCEAN (Big 5) and Myers Briggs Models of personality. It analyses the personality of each individual and then assigns them to an audience segment that can be customised to your brand’s pre-existing persona or tribe categories. It does this all while staying compliant with GDPR through a process of aggregation and anonymisation. The result is incredibly accurate and detailed profiles of your audience’s personalities, simultaneously matched against the tone of your brand’s content. This process used to involve a three-month extensive research period. This puzzling and labyrinthine procedure can now be truncated into a single month of effort, saving huge amounts of resources for all parties involved, along with more accurate results.

Contact us to learn more.

How To Turn Your Data Into Insights

By | How To

We swim in a sea of data. How can you take your data and turn it into intelligence you can use?

There are two main types of data: structured and unstructured. Structured data follows a specified format and requires processing to produce. Aggregated numbers/data points are a great example of structured data. Conversely, unstructured data doesn’t follow a specified format, but this is the individual data behind your structured data. This is text, tweets, photos that provide context to your structured data. Structured data provides you with the what is happening, while unstructured provides the why.

Both are important for turning data into insights. Here we share our tips for turning both types of data into insights in various areas that impact your brand or client.

Get started by aggregating all your data in one place here.

14 Insider Tips To Make Your Data Reliable

By | How To

Did you know businesses that engage in data-driven decision making enjoy a 5% increase in output and productivity. But how do you ensure you’re making decisions off reliable data? We've pulled together our tips for getting the most out of your data and best practices for data ethics.

Dive deeper into your data with us.

Buzz Radar, London // Lifestyle commercial and office photography //

How To Measure The Real Value of Social

By | How To

The importance of social media is well-stated. 71 percent of consumers follow businesses online for promotions, with 66 percent onboard for updates on new products. What’s more is that more than half of consumers will go online for customer service and 61 percent indicate reviews impact purchasing decisions. Social is now a crucial part of business.

While this is well-known, how does a business actually measure the efficacy of social? It allows for direct engagement with customers, the opportunity to build and expand brand presence, and sell more. There’s data for follower count, retweets, reach and more. Identifying which pieces of data matter is core to measuring social ROI. It’s more than likes and followers. Social media now allows brands to use more links, tag and even directly sell on platforms to create a frictionless purchasing opportunity and a better way to track the process.

So, how can your brand measure the real value of social?

      1. Think about evaluation at the start of campaigns.
        Design your measurements during the planning stages instead of at the tail end. While you may have to adjust campaign plans (by calibrating the weights of certain activities or introducing A/B tests), it’s much more effective to begin with the end in mind. The best way is to honestly and realistically define objectives, such as building brand awareness, driving leads, improving customer support or collecting consumer feedback.
      2. Start with the basics.
        Social is no different than other campaigns. Consider the purpose, priorities and timing of your evaluation. Clearly define which decisions your evaluation will inform and when you need the insights. There is no one size fits all approach. You will need to select the appropriate data and methodology for your brand and your campaign objectives. For example, if your main objective is brand awareness, you’ll want to look at community growth, as well as sentiment and share of voice. For lead generation, set up special links to track customer behavior. For customer support, compare dips in calls/emails versus rises with online requests or response times.
      3. Avoid silos and look for context.
        Social should never live alone. Social media is just one part of your marketing campaign and should be evaluated as such. This means that you’ll need to factor in both long and short-term goals. Activities with call-to-actions, for example, competitions calling for likes, will inevitably result in greater immediate response and online engagement, but they may not necessarily be set up to indicate long-term effects. In addition, looking at the data in context with other marketing objectives, you can see patterns and correlates that might be overlooked if data sits alone.
      4. Consider content classification for various goals.
        Some content or initiatives will be more effective for some objectives. A/B testing helps you measure this. You’ll learn what drives conversions and click-throughs, which, ultimately, enables you to understand the ROI on the time and creative spent on your campaigns.

While social continues to grow and become a part of marketing plans, knowing how to measure and interpret the data is key. We want to help you and your team make the most of real-time data.

For more on how we can help transform your business intelligence, head over to our Command Centre page.

The Trick To Understanding Sentiment

By | How To

If you’re in marketing, you’ve no doubt heard about sentiment. And you probably have your own views on it. It gets a bad rep, and in a lot of ways for the right reasons. But considering its widespread use, it’s worth digging a little deeper into the metric and seeing how to use it effectively and ethically.

Take this tweet. It actually ranks as negative sentiment yet, it more truly should be neutral to slightly positive. The main beef with sentiment is that most algorithms can’t fully master the nuances of speech. Sarcasm, in particular, is rather hard to get without full context.

Fortunately, technology is rapidly evolving, and sentiment is already bounds better than it was years ago. Sentiment is more than just a word for word analysis. Any sentiment analysis should include:

  • Sentiment shifters (e.g. “I find this tool less useful than yours”)
  • Connectives (e.g. “This tool is everything but useful”)
  • Modals (e.g. “In theory, this tool should be useful”)

Most tools now factor this in and allow for semantic interpretation and analysis. This gives you a great base. Sentiment really shines in showing you how to adapt, what your strengths and weaknesses really are, and where you stand. As much as sentiment tools are evolving, these are things that can really only be gleaned by human review. This review should look at

  • Topics: what are the main areas of discussion?
  • Aspects (subtopics and attributes): what about those topics is being talked about?
  • Sentiment: what is the sentiment of the content and the opinions contained?
  • Holder: whose opinion is being discussed? Are there multiple in the same content? If so, how do they differ, if at all?
  • Time: when was this content posted?

These factors help you know what to do with sentiment. Knowing your brand has a 60% positive sentiment ranking provides little insight. Knowing that the reason your brand has that sentiment score is because of excellent customer service helps you decide to put more resources into customer service.

So, don’t throw sentiment out with the bathwater. It can be a very valuable starting point for insight. Just know you have to dig a little deeper into it. We recognise that here at Buzz Radar, so we actually separate out our sentiment by platform and emotion to help you really understand the why. Coupled with Word Clouds and information on most influential posts, you can start to get a better sense of what’s really driving your sentiment score.

Be A Social Media Superstar: Your Cheat Sheet

By | How To

We know that we process visual info much more rapidly than text (60,000 times faster in fact). This means that imagery is crucial to marketing strategies. In the past, we’ve shared how to make a post more shareable, and images are a big part of this.

But it’s not just about adding in images. In order to create the best ads, you have to know the proper dimensions for each platform. OnBlastBlog has created the ultimate cheat sheet for this:

Social Media Cheat SheetCredit: On Blast Blog

Then, don’t forget to track the success of the posts with our Most Effective Posts feature. Get started today!

business intelligence

Use Your Words: How To Tweak Your Keyword Search

By | How To

Last year, we shared our four main tips for tracking data with keywords. And this remains one of the areas we get more inquiries for. How do you make your searches more effective? How do you adjust the search? So, we’re here to help guide you to success!

Let’s start with the basics — what exactly is a keyword? A keyword is basically a search term. It doesn’t always need to be a single word. It can be a sentence; it can be a hashtag or a mixture of letters and numbers. When you start a keyword search, you begin searching all the available platforms for every mention of the term to be analysed and visualised.

Searches can get more advanced when you use a query. So, what is a query? A query is used to search social media and real-time data sources for mentions of your topic/project. Often to get the best results and pull in the most relevant and interesting data, one keyword is not enough. In this case, you may need to use multiple keywords, keyword filters or exclusions. So, basically, the full set of keywords and filters is known as a query.

Now, if you want to get more advanced, you can use a filter. A keyword filter allows you to filter your keywords through another set of keywords. This enables you to narrow down the number of mentions you are pulling in and look at more specific data. For example, if you wanted to look for all mentions of Nike trainers, your query would be the keyword “Nike” and you would put “trainers”, “trainer” and “sneakers” in the Keyword Filters section. This way you would only pull in mentions of Nike in the context of trainer conversation. Simple, no?

One final layer is a keyword exclusion. A keyword exclusion stops keywords coming into query results. Sometimes you may find a keyword brings unwanted data associated with another keyword. So, if you put a term in the keyword exclusion, it will stop mentions of your keyword in unwanted contexts. For example, if you are looking for mentions of Nike but don’t want to see any sales related conversation, your query would be the keyword “Nike” and you would put “sale”, “sell” and “deal” in the Keyword Exclusions section. This way you would only pull in mentions of Nike which are not related to selling items.

Now, that we have the basics down, let’s get to actually constructing your query. The best way to start is to ask yourself what are you really looking for. When constructing your query, it is generally best to have an idea of what kind of data you are looking for. Start with these questions:

  1. Are you looking for data around a brand, a product, a general topic or a specific campaign?
  2. If you are interested in a brand, do they use any specific hashtags or phrases in their social media communication?
  3. Does the name of the brand have other meanings?

Then you need to know how to choose your keywords. If you are looking for a specific brand, it is best to start with their social platforms. Here you can see how they refer to themselves on social networks and work out if they are using any specific campaign hashtags. If you put their exact account names in as keywords, you will pull in all the directed messages towards their account. You may find though that a brand’s name has multiple meanings, in which case you can always use keyword filters to narrow down your search.

Using our Nike example, you would first take a look on Twitter search; and you will see that all the mentions of Nike refer to the brand and that they have a large number of Twitter accounts outside the main @Nike Twitter account. So, as well as “Nike”, you might want to also add @NikeStore and @NikeLab to get a wider picture of the Nike conversation.

As with most things, the key to success is testing! So, we encourage you to test those keywords. The quickest and easiest way to test a keyword is to put it in the Search Twitter box on Twitter and click the Live tab. This way you can get an overview of what kind of content your keyword will pull. If you see lots of irrelevant data, you should consider changing your keyword or using keyword filters.

Now, on occasion, you’ll find that your query brings in irrelevant data. Not to fret. There are a number of ways you can deal with this. For instance, if you wanted to measure Apple, a brand whose name has multiple meanings not all related to the brand, you might find a lot of mentions of fruit. In this case, you will want to use keyword filters. As you will most likely want all mentions of Apple’s products, you can put “iPhone” and “iMac” into the keywords and the keyword filters so you get all mentions. But as you would only want mentions of Apple in the context of the brand rather than the context of fruit, you would put Apple into the keywords but put terms related to Apple such as “computer” and “Tim Cook” in the keyword filters so only mentions related to the brand are pulled in.

Sometimes a brand’s name may have other meaning in different languages. For instance, the baby milk brand SMA is a word in Indonesian. To prevent from bringing in lots of irrelevant Indonesian tweets, you can set the language filter to English and all the Indonesian posts will not be aggregated with what you’re seeking.

Then, we have what we all love — spam. Very often a brand can be the victim of sales spam, hundreds of posts from re-sellers. You may want to individually block problem users, or you can put key sales keywords such as “eBay”, “sell” or “sale” words into the keyword exclusions.

At the end of the day, setting up a query is simple if you have the tools and a little bit of knowledge. And of course, our team is happy to help at any point. Now, go forth and query!