Plan and refine digital strategies by drawing on User Behavior data

Have you ever found yourself, as a Chief Digital Officer, E-commerce Manager, Product Manager, Digital Marketing Manager, Content Strategist, or SEO/PPC Specialist working on similar digital strategies?

1. Increase the variety and range of products offered on the e-commerce platform to attract a broader customer base and boost online sales

2. Innovate by creating new product features and services that meet emerging customer needs, or develop entirely new products to capture new market segments.

3. Create a comprehensive annual strategy for search promotion campaigns, including detailed media plans for optimizing search engine optimization (SEO) and pay-per-click (PPC) advertising channels.

4. Develop a strategic plan for the website’s page structure, content, and layout to enhance user experience, improve navigation, and increase engagement and conversion rates.

5. Establish realistic and measurable benchmarks for key performance indicators (KPIs) to monitor and evaluate the effectiveness of digital strategies and ensure continuous improvement.

These strategies share a common ground: a high level of uncertainty. To narrow this uncertainty, some replace it with the "sky finger" technique, meaning many specialists rely on gut feelings, team beliefs, or even stakeholders' or shareholders' beliefs. Other teams, mostly data-driven, rely on their organization's sales and promotion results from previous periods. However, this information is biased because it only reflects the results of interactions within the organization.

One approach to decrease bias and narrow uncertainty in such strategies is market research. This type of research should exclude organizational interference and reveal potential customer journey points (interest), existing demand, product categories, and their volume. Fortunately, we live in a digital era where search engines provide all the necessary information through keyword statistics, which are perfectly suited to our aims.

The first step in research analyzing online user behavior through search keywords is to understand data bias and identify reliable sources of information. The ROCCC framework (Relevance, Objectivity, Currency, Coverage, and Consistency) can be a valuable tool in determining where to source data

There are several tool like Moz Keyword, Similarweb, Semrush, Google Keyword Planner, Wordstream and many others. However just one of them is particularly valuable due to its original data, essential for getting more accurate keyword analysis and strategy development. It means just Google Keyword Planner fully ROCCC because it has first party data generated directly by Google Search.

Like most other tools, Google Keyword Planner has its own limitations, which can cause challenges when analyzing keyword statistics, especially for broad topics. To overcome the limitations on the number of keywords you can observe, it is advisable to separate the topic into several data files.

While comparing data sources by keyword number, average searches, and word count can tell us if there's a skew between them. We can also evaluate which source contributes the most significant data to the final results. Based on this analysis, we can then review what data points to filter out in future analyses.

Since related topics can encompass thousands of keywords, getting a general overview and understanding each data source can be challenging. However, by employing statistical methods and creating samples, we can gain valuable insights.

These samples can provide information about keyword meaning and the relationships between words across different data sources. This information helps us understand how to approach keywords from the perspective of the target audience: what they want to know and why they use specific words to form their search queries.

Every data analysis process involves technical data transformation steps, and this research is no exception. To proceed with further analysis, we need to consolidate data from all sources. This involves cleaning the data, verifying the schema (structure), specifying data types, and detecting any mismatches or errors. Ultimately, this process aims to refine the data and prepare it for loading into a database for further analysis.

Our initial step is to understand the overall relationships between data sources, keywords, and individual words within those keywords. This analysis aims to determine whether all keywords can be treated as a single dataset or if segmentation into different groups (clusters) is more appropriate.

The primary objective here is to identify the most frequently occurring words and compare their prevalence across different data sources. By analyzing these discrepancies, we can develop hypotheses to explain the observed variations and the underlying user behavior driving them.

In addition to analyzing individual keywords, we also aim to identify clusters of words within each data source. A higher level of overlap (intersection) between these word clusters across different sources suggests a lower degree of significant variation.

Based on this analysis, we can formulate hypotheses about whether merging data sources or treating them as a single dataset is the most appropriate approach

For any marketing strategy to be successful, it's crucial to understand where to allocate resources effectively and avoid wasted efforts. To achieve this, we need a closer look at the competitive landscape, specifically:

• Competitor Keyword Distribution: Analyzing how competitors distribute keywords across their content helps us identify potential opportunities to compete for user attention.

• Keyword Word Structure: Understanding the structure of keywords used by competitors can provide insights into the content development approach necessary to be competitive.

• Year-over-Year Keyword Trends: Examining how keywords are evolving over time can help us identify emerging trends and mitigate the risk of basing strategies on outdated consumer behavior.

By taking this comprehensive approach, we gain a clearer understanding of where to focus our content development efforts to effectively compete for user attention, while minimizing the risk of relying on outdated information.

Our previous research identified opportunities to group keywords based on their function within the user journey. For instance, "question words" often indicate users seeking answers and lacking a specific product in mind. Conversely, "product words" suggest users are in a solution-oriented phase and are looking for providers.

From a strategic standpoint, distinguishing keywords by functionality allows us to exclude those that don't represent our desired user behavior patterns (stop words). This targeted approach helps us refine our audience and eliminate irrelevant information (clutter) to reach the most relevant users.

Once we have generated hypotheses about core audience attributes such as search queries, competitor behavior, reflected words in search terms, keyword structure, and user behavior patterns, we can translate these insights into actionable data. This involves filtering out irrelevant data points and correcting any errors to create a clear picture of our target audience

The final research step involves creating a visual representation, or map, of the topic structure. Each topic on this map can be viewed as a distinct point within the customer journey.

This map offers several valuable insights:

• User Interest: We can estimate the size (level of interest) of each topic, allowing us to prioritize content creation efforts.

• Content and Product Messaging: By understanding the language users employ within each topic, we can tailor content and product descriptions to resonate with their specific needs.

• Website Architecture: The topic structure can inform website design, ensuring a user-friendly experience that guides users through their journey.

• User Communication: The map helps us identify key messages to communicate at each touchpoint to capture user attention and drive engagement.

Most importantly, this research generates actionable insights.  It tells us not only what to do, but also what to avoid. By following these insights, we can maximize the effectiveness of our marketing efforts.  Furthermore, by comparing our initial expectations with the achieved results, we can rigorously assess the success of our strategy.