averaging over large number of random realizations

Question

I want to run following for few hundreds of a random variable set. This calculates:

1. generate normal random variable
2. find gain with squaring
3. calculate performance metric
4. find max of min of possible M+1 performance metric when a changes (0,1). I have other case with another parameter b which changes (0,1).
5. find the average for 100 or 1000 random realizations
6. find same for different p values

Finally I want to save it as a table with different p and M.

variable[M_] := RandomVariate[NormalDistribution[0, 1], M + 1]; 
gain[M_, d_, t_] := variable[M]^2/(d/(M + 1))^t;
perform[p_, a_, m_, M_, d_, t_] := 
  1/(M + 1) p ((1 - a)/(2 - a)) a^(
   m - 1) (Product[gain[M, d, t][[i]], {i, 1, m}]);
maxminperform[p_, M_, d_, t_] := 
  Max[Table[
    Min[Table[perform[p, a, m, M, d, t], {m, 1, M + 1, 1}]], {a, 
     0.00001, 0.999, 0.001}]];
avgperform[p_, M_, d_, t_, itr_] := 
  Mean[Table[maxminperform[p, M, d, t], {i, 1, itr}]];

$ $

Module[{n = 0.001, η = 0.7, σ = 1, k = 7, d = 6, t = 3, 
  itr = 5}, 
 Export["table.xls", {Table[{avgperform[p, 2, d, t, itr], 
      avgperform[p, 4, d, t, itr], avgperform[p, 6, d, t, itr]}, {p, 
      1, 10, 1}] // N}]]

I understand that this takes really long time when I run.

Can someone help me to faster this code?