How do you understand your customer behavior with social media analytics?

The goals of social media marketing must be aligned with the overarching business objectives. It is worth organising workshops to define social media marketing goals, in which all key people take part and jointly define the goals, metrics and KPIs. This includes at least: 1. Someone who understands the business objectives and strategies, with input from Marketing and Finance (C-level, Sr. Executives) 2. Someone who is familiar with web analytics (data analyst) 3. Someone who can integrate the defined tracking and knows the data environment (developer) The identified goals, key performance indicators and metrics should be visualised in a social media marketing measurement model and made available to everyone.

Start by asking: What do I want to achieve? Whether it's increasing brand awareness, driving sales, or improving customer service, your objectives guide your analysis. For instance, tracking engagement metrics like shares and comments can illuminate content preferences, while conversion rates from social media ads reveal the effectiveness of your marketing strategies. Establish clear, measurable goals to unlock actionable insights and tailor your approach to meet your audience's needs.

Identify the specific objectives you aim to achieve, whether it's increasing brand awareness, driving engagement, or improving customer satisfaction. These goals will serve as the foundation for your social media analytics strategy, guiding the selection of relevant metrics and key performance indicators (KPIs). By aligning your analytics efforts with well-defined goals, you can focus on extracting meaningful insights that provide a comprehensive understanding of customer behavior on social media platforms.

Leverage social media analytics to track customer engagement, analyze post interactions, measure sentiment, and identify patterns. This provides insights into customer preferences, behavior, and sentiments, aiding in informed decision-making and targeted strategies.

Use social media analytics to track key metrics like engagement, reach, and click-through rates. Analyze demographic data to understand your audience. Monitor content performance to identify popular topics and formats. Track the growth of your follower base and assess the impact of campaigns. Utilize sentiment analysis to gauge audience reactions. Identify peak engagement times for strategic posting. Pay attention to comments and direct messages for customer feedback. Regularly review analytics data to refine your social media strategy and better cater to customer preferences and behaviors.

When you choose your metrics don't just lean on ones that you think are important. Instead, start by thinking about what your goals are and let those inform your KPIs. For example, impressions are great if your goal is to increase awareness so a post would be successful if it had high impressions even if it had lower clicks. If you're after conversions, though, then all of the impressions in the world won't matter if you're not getting clicks.

In my experience, choosing the right metrics is crucial in understanding customer behavior through social media analytics. Engagement rate, including likes, shares, and comments, reveals what resonates with your audience. Click-through rates indicate the effectiveness of calls to action, guiding content strategy. Customer sentiment analysis, through comments and mentions, provides insights into their perceptions and needs. Focusing on these metrics, along with demographic data, helps tailor content and strategies to enhance customer engagement and satisfaction.

Understanding customer behavior through social media analytics begins with selecting the right tools. Key platforms like Google Analytics, Hootsuite, or Sprout Social offer deep insights into user engagement, demographics, and content performance. One thing I've found helpful is setting clear KPIs related to your goals—be it increasing brand awareness or boosting sales. Analyzing trends, monitoring mentions, and tracking hashtag performance can unveil patterns in consumer behavior, guiding more informed marketing strategies. Start by integrating these tools into your workflow to gather actionable data.

It is completely undervalued the ability to code a custom analytics strategy. This kind of quality statistical analysis is not available in most if not all of the current social media tools on the market. All of these tools have simple equations written into their algorithms and can't execute multi-variate equations. If you want to look at multiple variables' effect upon one another (for example how 1) women 2) age 30-40 3) engagement 4) carousel 5) climate change), you could never do this with a general tool, but would have to write a custom regression or correlation equation in SPSS or a similar statistical software. Thus DO NOT UNDERESTIMATE the power of someone who truly understands how to analyze large sets of variate data!

Please DO NOT draw conclusions for your social media strategy based on improper A/B testing or based on just two points of data. Proper A/B testing means comparing only ONE CHANGE between two posts. No that does not mean you can change the image type and the whole caption as long as it's on the same topic. That means only change the image and keep the same caption, at the same time, etc. If you change too many variables on an A/B test it is invalid. Delve into ENOUGH data points that you can draw a significant correlation. You can not make a valid conclusion about a type of post being "better" than another by comparing two data points. Use your social tools to code metadata into your posts so you can compare multiple variables across time.

I use social media analytics knowing its limitations. I use my own background in research science to code my own multi-variate regressions and correlations to see how multiple variables interact with each other to better understand my audience. I can see information like if (numbers for coded variables) 1) women in 2) Jamaica 3) click-through 4) Facebook Video 5) climate-change and then can exchange variables to test across different platforms, topics, and audiences. It's important to not draw general conclusions based on inconclusive or insufficient data. Based on these insights I adjust my messaging on each platform for organic posts, but I have found really impressive results adjusting my strategy after this type of analysis for ads.