{"id":37,"date":"2026-05-31T04:39:21","date_gmt":"2026-05-31T04:39:21","guid":{"rendered":"https:\/\/charithheenpalla.com\/library\/?p=37"},"modified":"2026-05-31T05:20:38","modified_gmt":"2026-05-31T05:20:38","slug":"markovian-chain-model-for-marketers","status":"publish","type":"post","link":"https:\/\/charithheenpalla.com\/library\/2026\/05\/31\/markovian-chain-model-for-marketers\/","title":{"rendered":"Markovian Chain Model for B2B Marketers"},"content":{"rendered":"\n

1. The Markov model in marketing<\/strong><\/h2>\n\n\n\n

In marketing analytics \u2014 especially multi-touch attribution<\/strong>, customer journey modeling<\/strong>, and lead nurturing<\/strong> \u2014 a Markov chain<\/strong> is used to model how prospects move through states<\/strong> (touchpoints) before converting or dropping off.<\/p>\n\n\n\n

Each state<\/em> might be something like:<\/p>\n\n\n\n

Email click \u2192 Website visit \u2192 Whitepaper download \u2192 Sales call \u2192 Purchase<\/p>\n\n\n\n

The Markov assumption<\/strong> says:<\/p>\n\n\n\n

The next step a customer takes depends only on their current touchpoint, not on the entire path they took to get there.<\/p>\n\n\n\n

So in formula form:<\/p>\n\n\n\n

P(next step\u2223current step,previous steps)=P(next step\u2223current step)P(next step\u2223current step,previous steps)=P(next step\u2223current step)<\/p>\n\n\n\n

This means if someone is at \u201cDemo requested\u201d<\/em>, we don\u2019t need to know whether they came from organic search<\/em> or LinkedIn ad<\/em> \u2014 for prediction or attribution purposes, their current engagement level<\/em> carries all the relevant information.<\/p>\n\n\n\n

\n\n\n\n

2. In B2B marketing<\/strong><\/h2>\n\n\n\n

In B2B<\/strong>, the buyer journey is long, with many nurturing steps. Markov models help you estimate:<\/p>\n\n\n\n

    \n
  • Which touchpoints (ads, events, emails, etc.) are most likely to lead to conversion.<\/li>\n\n\n\n
  • The removal effect<\/em>: if we removed a touchpoint, how much conversion probability would drop.<\/li>\n<\/ul>\n\n\n\n

    Example:<\/strong>
    <\/strong>If removing \u201cWebinar attendance\u201d drops conversion rate from 12% \u2192 7%, that touchpoint has high influence \u2014 even if it wasn\u2019t the final step.<\/p>\n\n\n\n

    Here the Markov idea fits perfectly:
    Each account\u2019s next move<\/em> (e.g. scheduling a demo, going cold, etc.) depends mostly on their current engagement state<\/em>, not the entire history.<\/p>\n\n\n\n

    \n\n\n\n

    3. In SEO (Search Engine Optimization)<\/strong><\/h2>\n\n\n\n

    SEO behaviors can be modeled as Markov transitions too:<\/p>\n\n\n\n

    Impression \u2192 Click \u2192 Engagement \u2192 Conversion<\/p>\n\n\n\n

    Here, \u201cEngagement\u201d is the current state, and what matters is the probability of moving to \u201cConversion.\u201d
    You don\u2019t need to model every previous keyword<\/em> or entry page<\/em>, because the current state (user intent, engagement depth, dwell time)<\/strong> already encodes that.<\/p>\n\n\n\n

    This helps in predicting:<\/p>\n\n\n\n

      \n
    • Conversion likelihood from certain types of landing pages.<\/li>\n\n\n\n
    • How internal linking or UX improvements change transition probabilities.<\/li>\n<\/ul>\n\n\n\n
      \n\n\n\n

      4. In Relationship marketing<\/strong><\/h2>\n\n\n\n

      In relationship or retention marketing, Markov models can track state transitions over time<\/strong> such as:<\/p>\n\n\n\n

      Active \u2192 Engaged \u2192 Dormant \u2192 Lost \u2192 Reactivated<\/p>\n\n\n\n

      The model assumes the customer\u2019s next behavior<\/em> depends primarily on their current relationship state<\/em>.<\/p>\n\n\n\n

      So if a client is \u201cDormant\u201d<\/em>, your system predicts their next likely state (either \u201cLost\u201d or \u201cReactivated\u201d) without needing their full activity history.
      Then you design interventions (emails, calls, offers) that increase the probability of transitioning back to \u201cEngaged.\u201d<\/em><\/p>\n\n\n\n

      \n\n\n\n

      5. Why this works conceptually<\/strong><\/h2>\n\n\n\n

      The Markov assumption works in marketing because:<\/p>\n\n\n\n

        \n
      • The current observable data<\/strong> (like engagement score, recent click, or last activity) already compresses most of the relevant past behavior<\/em>.<\/li>\n\n\n\n
      • In practice, marketers don\u2019t have infinite data, so Markovian simplification<\/em> is a practical and powerful approximation.<\/li>\n<\/ul>\n\n\n\n
        \n\n\n\n

        Example summary table<\/strong><\/h2>\n\n\n\n
        Marketing Area<\/strong><\/td>Example States<\/strong><\/td>What \u201cMarkov\u201d means here<\/strong><\/td><\/tr>
        B2B Marketing<\/td>Awareness \u2192 Engagement \u2192 Demo \u2192 Sale<\/td>Next step depends on current engagement, not full journey<\/td><\/tr>
        SEO<\/td>Impression \u2192 Click \u2192 Read \u2192 Conversion<\/td>Current engagement level predicts next action<\/td><\/tr>
        Relationship Marketing<\/td>Active \u2192 Dormant \u2192 Lost \u2192 Reactivated<\/td>Future state depends only on current loyalty status<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

        1. Define your states<\/strong><\/h2>\n\n\n\n

        Pick the \u201cplaces\u201d a lead\/customer can be. For a B2B-ish journey:<\/p>\n\n\n\n

          \n
        1. Start (first touch \u2014 could be SEO visit, ad click, etc.)<\/li>\n\n\n\n
        2. Content (they read blog \/ downloaded asset)<\/li>\n\n\n\n
        3. MQL (marketing-qualified lead)<\/li>\n\n\n\n
        4. SQL (sales-qualified \u2014 meeting booked)<\/li>\n\n\n\n
        5. Won (conversion)<\/li>\n\n\n\n
        6. Drop (they disengaged)<\/li>\n<\/ol>\n\n\n\n

          Markov chains also like having absorbing<\/strong> states \u2014 ones you can enter but not leave \u2014 like Won and Drop.<\/p>\n\n\n\n

          \n\n\n\n

          2. Collect paths<\/strong><\/h2>\n\n\n\n

          You need user journeys like:<\/p>\n\n\n\n

            \n
          • Start \u2192 Content \u2192 MQL \u2192 SQL \u2192 Won<\/li>\n\n\n\n
          • Start \u2192 Content \u2192 Drop<\/li>\n\n\n\n
          • Start \u2192 MQL \u2192 Drop<\/li>\n\n\n\n
          • Start \u2192 Content \u2192 SQL \u2192 Drop<\/li>\n<\/ul>\n\n\n\n

            You can get these from analytics (pageviews \u2192 form fills \u2192 CRM stages).<\/p>\n\n\n\n

            \n\n\n\n

            3. Count transitions<\/strong><\/h2>\n\n\n\n

            From those journeys, count how often you go from one state to another.<\/p>\n\n\n\n

            Example toy counts:<\/p>\n\n\n\n

              \n
            • From Start: 80 \u2192 Content, 20 \u2192 Drop<\/li>\n\n\n\n
            • From Content: 50 \u2192 MQL, 30 \u2192 Content (they browse more), 20 \u2192 Drop<\/li>\n\n\n\n
            • From MQL: 40 \u2192 SQL, 60 \u2192 Drop<\/li>\n\n\n\n
            • From SQL: 30 \u2192 Won, 70 \u2192 Drop<\/li>\n\n\n\n
            • Won \u2192 Won (stay)<\/li>\n\n\n\n
            • Drop \u2192 Drop (stay)<\/li>\n<\/ul>\n\n\n\n
              \n\n\n\n

              4. Turn counts into probabilities<\/strong><\/h2>\n\n\n\n

              Just normalize each row so it sums to 1.<\/p>\n\n\n\n

              For example, for Content:<\/p>\n\n\n\n

                \n
              • total = 50 + 30 + 20 = 100
                So:<\/li>\n\n\n\n
              • P(Content\u2192MQL) = 0.50<\/li>\n\n\n\n
              • P(Content\u2192Content) = 0.30<\/li>\n\n\n\n
              • P(Content\u2192Drop) = 0.20<\/li>\n<\/ul>\n\n\n\n

                Do this for every state, and you get a transition matrix<\/strong>.<\/p>\n\n\n\n

                Here\u2019s what it might look like:<\/p>\n\n\n\n

                From \\ To<\/strong><\/td>Start<\/strong><\/td>Content<\/strong><\/td>MQL<\/strong><\/td>SQL<\/strong><\/td>Won<\/strong><\/td>Drop<\/strong><\/td><\/tr>
                Start<\/strong><\/td>0<\/td>0.80<\/td>0<\/td>0<\/td>0<\/td>0.20<\/td><\/tr>
                Content<\/strong><\/td>0<\/td>0.30<\/td>0.50<\/td>0<\/td>0<\/td>0.20<\/td><\/tr>
                MQL<\/strong><\/td>0<\/td>0<\/td>0<\/td>0.40<\/td>0<\/td>0.60<\/td><\/tr>
                SQL<\/strong><\/td>0<\/td>0<\/td>0<\/td>0<\/td>0.30<\/td>0.70<\/td><\/tr>
                Won<\/strong><\/td>0<\/td>0<\/td>0<\/td>0<\/td>1.00<\/td>0<\/td><\/tr>
                Drop<\/strong><\/td>0<\/td>0<\/td>0<\/td>0<\/td>0<\/td>1.00<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

                That\u2019s a Markov chain.<\/p>\n\n\n\n

                \n\n\n\n

                5. What can you do with it?<\/strong><\/h2>\n\n\n\n

                a. Predict likelihood of conversion<\/strong><\/h3>\n\n\n\n

                If everyone starts at Start, you can simulate \u201cwhat % eventually end up in Won?\u201d. That gives you a data-driven funnel performance number.<\/p>\n\n\n\n

                b. Removal effect (classic in marketing Markov attribution)<\/strong><\/h3>\n\n\n\n

                This is where it ties to SEO, webinars, email, etc.<\/p>\n\n\n\n

                  \n
                1. Compute conversion rate with the full chain.<\/li>\n\n\n\n
                2. Remove<\/strong> one touchpoint\/state (e.g. Content \u2014 maybe that\u2019s your SEO traffic) by rerouting its transitions.<\/li>\n\n\n\n
                3. Recompute conversion rate.<\/li>\n\n\n\n
                4. The drop in conversions = the contribution of that touchpoint.<\/li>\n<\/ol>\n\n\n\n

                  That\u2019s how Markov attribution tools say things like \u201cSEO landing pages contribute 24% of conversions.\u201d<\/p>\n\n\n\n

                  \n\n\n\n

                  6. Python version (so you can actually run it)<\/strong><\/h2>\n\n\n\n

                  import numpy as np<\/p>\n\n\n\n

                  states = [“Start”, “Content”, “MQL”, “SQL”, “Won”, “Drop”]<\/p>\n\n\n\n

                  idx = {s: i for i, s in enumerate(states)}<\/p>\n\n\n\n

                  # transition matrix from the table above<\/p>\n\n\n\n

                  P = np.array([<\/p>\n\n\n\n

                      [0.0, 0.8, 0.0, 0.0, 0.0, 0.2],  # Start<\/p>\n\n\n\n

                      [0.0, 0.3, 0.5, 0.0, 0.0, 0.2],  # Content<\/p>\n\n\n\n

                      [0.0, 0.0, 0.0, 0.4, 0.0, 0.6],  # MQL<\/p>\n\n\n\n

                      [0.0, 0.0, 0.0, 0.0, 0.3, 0.7],  # SQL<\/p>\n\n\n\n

                      [0.0, 0.0, 0.0, 0.0, 1.0, 0.0],  # Won (absorbing)<\/p>\n\n\n\n

                      [0.0, 0.0, 0.0, 0.0, 0.0, 1.0],  # Drop (absorbing)<\/p>\n\n\n\n

                  ])<\/p>\n\n\n\n

                  # start everyone in “Start”<\/p>\n\n\n\n

                  start_dist = np.zeros(len(states))<\/p>\n\n\n\n

                  start_dist[idx[“Start”]] = 1.0<\/p>\n\n\n\n

                  # iterate the chain a few steps to see where people end up<\/p>\n\n\n\n

                  dist = start_dist.copy()<\/p>\n\n\n\n

                  for _ in range(10):  # enough steps to reach absorbing states<\/p>\n\n\n\n

                      dist = dist @ P<\/p>\n\n\n\n

                  print(“Final distribution:”, dict(zip(states, dist)))<\/p>\n\n\n\n

                  You\u2019ll see most mass ends up in Drop, some in Won. That \u201csome\u201d is your expected conversion rate from that journey structure.<\/p>\n\n\n\n

                  \n\n\n\n

                  Where SEO \/ Relationship \/ B2B slot in<\/strong><\/h2>\n\n\n\n
                    \n
                  • SEO<\/strong> \u2192 often your \u201cStart\u201d or \u201cContent\u201d state. Markov tells you: \u201cIf I remove SEO visits, how many journeys die?\u201d<\/li>\n\n\n\n
                  • B2B nurturing<\/strong> \u2192 the middle states (Content, MQL, SQL) \u2014 Markov tells you which nurturing step actually advances people.<\/li>\n\n\n\n
                  • Relationship marketing \/ retention<\/strong> \u2192 same idea, but states become Active \u2192 At risk \u2192 Churned \u2192 Reactivated. You can then test: \u201cIf I remove the quarterly check-in email, how many more churn?\u201d<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"

                    1. The Markov model in marketing In marketing analytics \u2014 especially multi-touch attribution, customer journey modeling, and lead nurturing \u2014…<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-37","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/charithheenpalla.com\/library\/wp-json\/wp\/v2\/posts\/37","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/charithheenpalla.com\/library\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/charithheenpalla.com\/library\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/charithheenpalla.com\/library\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/charithheenpalla.com\/library\/wp-json\/wp\/v2\/comments?post=37"}],"version-history":[{"count":2,"href":"https:\/\/charithheenpalla.com\/library\/wp-json\/wp\/v2\/posts\/37\/revisions"}],"predecessor-version":[{"id":49,"href":"https:\/\/charithheenpalla.com\/library\/wp-json\/wp\/v2\/posts\/37\/revisions\/49"}],"wp:attachment":[{"href":"https:\/\/charithheenpalla.com\/library\/wp-json\/wp\/v2\/media?parent=37"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/charithheenpalla.com\/library\/wp-json\/wp\/v2\/categories?post=37"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/charithheenpalla.com\/library\/wp-json\/wp\/v2\/tags?post=37"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}