You need trustworthy campaign attribution models to know where to invest your sales and marketing budget, but most attribution tools fall short because they assume that you have perfect data.
In the real world, duplicate leads and contacts, opportunities without any contacts, and dirty data can sabotage your best efforts. Openprise sophisticated campaign attribution tools overcome those hurdles to give you new insights into where to make your next sales and marketing investment.
Say goodbye to garbage-in/garbage-out—just take out the garbage
- Dedupe: Avoid double counting campaigns across a duplicate record. Merge duplicate leads and contacts to associate all your campaigns with the right person in your opportunities, and give them the correct attribution.
- Assign contacts to opportunities: Automatically assign campaign members to corresponding opportunities based on your logic. No more opps without contacts.
- Attribute leads to the right opportunity—even before converting them to contacts.
Create the right campaign attribution models: out-of-the-box or custom-fit
- Use first touch, last touch, and multi-touch—in any combination.
- Tailor attribution models to fit the needs of your business.
- Push data-relevant fields to your CRM or marketing automation platform of choice to give stakeholders a holistic view within one application.
- Standardize company names, such as “GE” vs. “General Electric” using algorithms with fuzzy matching.
- Normalize phone numbers in every country, based on the right local format, so auto dialers work flawlessly.
- Normalize state and country fields to two-letter codes or spelled out, even with data in local languages and double-byte character sets, so segmentation is a breeze.
Get the full story with powerful reporting and visuals
- See the impact of every campaign on your top line.
- Learn how each touch moves deals through your pipeline.
Get all the capabilities you need to automate your Campaign attribution
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Attribution FAQ
1. What is a sales attribution model, and why is it important?
A sales attribution model is a framework that determines how credit for a sale or conversion is distributed across various marketing touchpoints that a customer encounters. Attribution models help businesses understand which marketing activities are most effective in driving revenue. By assigning credit to specific campaigns, channels, or interactions, companies can allocate their resources more efficiently and improve decision-making for future strategies.
2. What are the main types of sales attribution models?
The primary types of sales attribution models include first-touch, last-touch, linear, time-decay, and position-based (U-shaped) attribution. First-touch gives all credit to a customer’s initial interaction with your brand, while last-touch credits the final interaction before conversion. Linear attribution spreads credit evenly across all touchpoints. Time-decay assigns more value to touches closer to the conversion event, and position-based models highlight the first and last interactions as more influential, with some credit to the middle steps.
3. How do I choose the right attribution model for my business?
To select the best attribution model, consider your sales cycle length, the number of customer touchpoints, the mix of online and offline channels, and your ability to track interactions accurately. Companies with complex, long sales cycles and many touchpoints often benefit from multi-touch or data-driven models, while those with shorter, simpler funnels may use single-touch attribution for simplicity. Testing different models and comparing their insights can help find the best fit for your strategy.
4. What are the limitations and challenges of sales attribution models?
No attribution model is perfect. Single-touch models can miss the influence of other channels, while multi-touch models may overcomplicate analysis or rely on incomplete data. Attribution accuracy is limited by data quality, tracking gaps, and offline interactions. Models that don’t factor in all relevant customer behaviors risk giving misleading results. It’s important to combine attribution findings with qualitative feedback and ongoing measurement to refine your approach over time.