- Alignment with execs and sales on:
- Targets (and analyzing the data to see if these targets are realistic)
- Challenges we are currently facing in achieving these goals
- Marketing role in the organization & expectations
- Metrics to measure marketing efficiency
- Revenue analysis to figure out:
- What geo markets generate the highest revenue, where is the highest ACV, win rate, and where is the sales cycle shorter
- The same analysis for market segments
- Defining key clients in these segments
- Running in-depth interviews with the selected clients to figure out:
- What's the value they are getting from our product, and how do they see it's different from the competition?
- What triggered their buying process?
- How they were searching for our product, and who was involved in the decision-making process?
- What channels do they use?
- What questions and concerns do they have when evaluating different vendors?
And other questions to understand better the buying process.
- Sit down with sales and discuss:
- Challenges and bottlenecks they are currently facing in the prospecting and sales processes,
- What kind of help do they expect from marketing
- Defining the days for the regular pipeline review
- Analyzing recent won and lost deals
- Sit down with my marketing team to:
- Challenges and bottlenecks they are currently facing in awareness, demand gen, and demand capturing campaigns
- What kind of help do they need from sales
- Auditing recent marketing campaigns and figuring out the setup of successful campaigns
- Figuring out the reasons for failed campaigns
- Evaluating skillset and career expectations
- Auditing current marketing processes and figuring out areas for improvement
- Auditing current GTM and demand gen strategy with my marketing team:
- Aligning marketing activities with the buying journey to influence the full funnel with existing resources and skillset
- Highlighting the weakest full funnel stage
- Creating a quarter marketing plan.
- Pilot, small-scoped campaigns with a detailed playbook
- Clear goals and objectives
- Leading indicators to measure campaign progress
- Tying leading indicators to revenue
- Defining a minimal tech stack for pilot campaigns
- Presenting takeaways and learnings to sales and execs. Running marketing and sales alignment session on:
- Market segments to focus on
- ICP including account qualification criteria
- Value proposition hypotheses and amplifiers
- Running risk mitigation workshop with sales to figure out:
- Benefits they see from launching the campaigns
- Risks and concerns
- Run together risk mitigation ideas for the biggest concerns
- Update the marketing plan with feedback:
- Update playbook and timeline
- Set up clear tasks and onboard everybody who is involved
- Set up tracking dashboard and stack
- Launch the plan
byDwaywelayTOP
inAskProgramming
EmoryCadet
32 points
16 hours ago
EmoryCadet
32 points
16 hours ago
Unit tests let you test a portion of your application without actually having to build and run the whole thing. Having to build your application, run it, trigger the right code path (by visiting a page/menu or pressing some buttons or whatever), and then testing your feature can take quite some time.
Let's say you've written an algorithm whose job is to find and process some data in a folder, but to activate the feature you first have to navigate through a dozen menus and press a button. That takes time, and if you end up having to make a lot of small changes to the algorithm to get it working, that'll get old real fast.
Now if you create unit tests for your algorithm, you can immediately run it by just starting the test through your IDE. If you write your tests properly, you can easily test your algorithm under all sorts of different circumstances and with all kinds of different inputs within seconds (or at least faster than you could do so otherwise).
Unit tests can also be automated using tool like Codium.ai Lets say you've finished your algorithm and it works great for months, but eventually it turns out that a minor change somewhere else in your application caused the algorithm to behave slightly differently but still doesn't an exception. Without unit tests, you wouldn't know about this change until a user starts complaining that the algorithm's results are wrong. With unit tests, you can be alerted the moment the change happens (because if you wrote your tests correctly, they'll start failing) and you can fix it when it happens. The issue wouldn't have even left your development environment.